scan-v5.json (26996B)
1 { 2 "scan_version": 5, 3 "paper_type": "empirical", 4 "paper": { 5 "title": "On Evaluating the Efficiency of Source Code Generated by LLMs", 6 "authors": [ 7 "Changan Niu", 8 "Ting Zhang", 9 "Chuanyi Li", 10 "Bin Luo", 11 "Vincent Ng" 12 ], 13 "year": 2024, 14 "venue": "2024 IEEE/ACM First International Conference on AI Foundation Models and Software Engineering (Forge)", 15 "arxiv_id": "2404.06041", 16 "doi": "10.1145/3650105.3652295" 17 }, 18 "checklist": { 19 "claims_and_evidence": { 20 "abstract_claims_supported": { 21 "applies": true, 22 "answer": true, 23 "justification": "Abstract claims evaluation on HumanEval/MBPP and LeetCode, and prompting strategies are all demonstrated in Sections 2.1 and 2.2 with supporting evidence.", 24 "source": "haiku" 25 }, 26 "causal_claims_justified": { 27 "applies": true, 28 "answer": true, 29 "justification": "RQ2 tests causal relationship between prompts and code efficiency via controlled experiments across three prompt variants (Figure 3, Table 4), showing differential effects.", 30 "source": "haiku" 31 }, 32 "generalization_bounded": { 33 "applies": true, 34 "answer": true, 35 "justification": "Results scoped to three benchmarks (HumanEval, MBPP, LeetCodeEval) and Python/C++ respectively. Paper acknowledges differences across benchmarks (Section 2.1.5: 'LLM performs differently across benchmarks').", 36 "source": "haiku" 37 }, 38 "alternative_explanations_discussed": { 39 "applies": true, 40 "answer": true, 41 "justification": "Paper discusses why prompting works better on LeetCode (more diverse test cases), why training strategy affects efficiency (DeepSeek Base vs Instruct), and attributes benchmark differences to data distribution.", 42 "source": "haiku" 43 }, 44 "proxy_outcome_distinction": { 45 "applies": true, 46 "answer": true, 47 "justification": "Paper explicitly measures runtime via gem5 simulator (HumanEval/MBPP) and LeetCode platform submissions. Clear distinction between correctness (Pass@10) and efficiency (runtime) metrics reported separately.", 48 "source": "haiku" 49 } 50 }, 51 "limitations_and_scope": { 52 "limitations_section_present": { 53 "applies": true, 54 "answer": true, 55 "justification": "Section 3 'Threats to Validity' is dedicated to limitations, discussing data leakage, runtime instability, and mitigation strategies.", 56 "source": "haiku" 57 }, 58 "threats_to_validity_specific": { 59 "applies": true, 60 "answer": true, 61 "justification": "Specific threats identified: (1) data leakage mitigated by selecting LeetCode problems post-May 2023 cutoff; (2) runtime instability mitigated via gem5 simulator and 10 repeated runs.", 62 "source": "haiku" 63 }, 64 "scope_boundaries_stated": { 65 "applies": true, 66 "answer": true, 67 "justification": "Paper states C++ focus for LeetCodeEval, acknowledges hard subset cannot be evaluated (0 problems passing all models), and notes results only for problems where all LLMs pass.", 68 "source": "haiku" 69 } 70 }, 71 "conflicts_of_interest": { 72 "funding_disclosed": { 73 "applies": true, 74 "answer": true, 75 "justification": "Acknowledgments state support from 'Cooperation Fund of Huawei-NJU Creative Laboratory', 'CCF-Huawei Populus Grove Fund', and 'NSF award 2034508'.", 76 "source": "haiku" 77 }, 78 "affiliations_disclosed": { 79 "applies": true, 80 "answer": false, 81 "justification": "Author affiliations listed (Nanjing University, Singapore Management University, UT Dallas) but no disclosure whether any authors are affiliated with evaluated model providers (OpenAI, Meta, Microsoft, DeepSeek).", 82 "source": "haiku" 83 }, 84 "funder_independent_of_outcome": { 85 "applies": true, 86 "answer": true, 87 "justification": "NSF funding is independent. Huawei funding is manufacturer but not provider of evaluated LLMs, so reasonable independence, though Huawei could benefit from benchmark insights.", 88 "source": "haiku" 89 }, 90 "financial_interests_declared": { 91 "applies": true, 92 "answer": false, 93 "justification": "No competing interests statement provided. No declaration of patents, equity, consulting relationships, or other financial interests.", 94 "source": "haiku" 95 } 96 }, 97 "scope_and_framing": { 98 "key_terms_defined": { 99 "applies": true, 100 "answer": true, 101 "justification": "Paper defines 'efficiency' as runtime (measured via gem5 or LeetCode platform), formally defines 'average normalized runtime' metric and 'Pass@10' metric in Section 2.1.4.", 102 "source": "haiku" 103 }, 104 "intended_contribution_clear": { 105 "applies": true, 106 "answer": true, 107 "justification": "Three explicit contributions stated in introduction: (1) evaluate LLM code efficiency, (2) propose LeetCodeEval benchmark, (3) investigate prompting strategies for efficient code generation.", 108 "source": "haiku" 109 }, 110 "engagement_with_prior_work": { 111 "applies": true, 112 "answer": true, 113 "justification": "Related Work section cites DeepDev-PERF, Madaan et al.'s PIE work on code optimization, Self-Refine, and code quality evaluation papers, explaining how this work differs (efficiency focus vs quality focus).", 114 "source": "haiku" 115 } 116 } 117 }, 118 "type_checklist": { 119 "empirical": { 120 "artifacts": { 121 "code_released": { 122 "applies": true, 123 "answer": true, 124 "justification": "Paper states 'We also make code, data and other artifacts available online' with GitHub reference [1] pointing to https://github.com/NougatCA/EfficencyEval.", 125 "source": "haiku" 126 }, 127 "data_released": { 128 "applies": true, 129 "answer": true, 130 "justification": "HumanEval and MBPP are publicly available benchmarks. LeetCodeEval problem selection and raw results claimed to be on GitHub. Public benchmark data is accessible.", 131 "source": "haiku" 132 }, 133 "environment_specified": { 134 "applies": true, 135 "answer": false, 136 "justification": "Gem5 simulator used but no configuration details, version, or specifications provided. No requirements.txt, Dockerfile, or dependency specification for reproduction.", 137 "source": "haiku" 138 }, 139 "reproduction_instructions": { 140 "applies": true, 141 "answer": true, 142 "justification": "Steps described: generate k responses, execute via gem5/LeetCode, repeat 10 times for HumanEval/MBPP or 3 times for LeetCodeEval. GitHub repo likely contains detailed scripts but paper provides outline.", 143 "source": "haiku" 144 } 145 }, 146 "statistical_methodology": { 147 "confidence_intervals_or_error_bars": { 148 "applies": true, 149 "answer": false, 150 "justification": "Tables 2, 3, 4 report single averaged values (average normalized runtime, speedup) with no confidence intervals or error bars despite running evaluations 10 times.", 151 "source": "haiku" 152 }, 153 "significance_tests": { 154 "applies": true, 155 "answer": false, 156 "justification": "Paper makes comparative claims ('GPT-4 has highest efficiency', 'Prompt 3 best for medium problems') without reporting p-values or statistical significance tests.", 157 "source": "haiku" 158 }, 159 "effect_sizes_reported": { 160 "applies": true, 161 "answer": true, 162 "justification": "Speedup rates reported in Table 4 (e.g., 1.06x for GPT-4 Prompt 1) serve as effect sizes. Normalized runtime comparisons show relative magnitudes.", 163 "source": "haiku" 164 }, 165 "sample_size_justified": { 166 "applies": true, 167 "answer": false, 168 "justification": "Paper acknowledges only 70/164 HumanEval and 242/399 MBPP problems pass all models, making sample very small for comparisons. No power analysis or sample size justification provided.", 169 "source": "haiku" 170 }, 171 "variance_reported": { 172 "applies": true, 173 "answer": false, 174 "justification": "Despite running each evaluation 10 times, paper reports only average runtime values in tables. Standard deviation, variance across runs, or confidence intervals are not reported.", 175 "source": "haiku" 176 } 177 }, 178 "evaluation_design": { 179 "baselines_included": { 180 "applies": true, 181 "answer": true, 182 "justification": "RQ1 evaluates 6 different models (GPT-4, GPT-3.5, Phi-2, Code Llama, WizardCoder, DeepSeek) as baselines for comparison. RQ2 compares 3 prompt variants against baseline.", 183 "source": "haiku" 184 }, 185 "baselines_contemporary": { 186 "applies": true, 187 "answer": true, 188 "justification": "Models from 2023-2024 (GPT-4-1106-preview, Code Llama 2023, DeepSeek Coder 2024) are contemporary with the 2024 paper publication.", 189 "source": "haiku" 190 }, 191 "ablation_study": { 192 "applies": true, 193 "answer": true, 194 "justification": "RQ2 ablates prompting strategy across three variants (direct instruction vs two chain-of-thought approaches), showing impact of prompting method on efficiency.", 195 "source": "haiku" 196 }, 197 "multiple_metrics": { 198 "applies": true, 199 "answer": true, 200 "justification": "RQ1 uses normalized runtime and Pass@10. RQ2 uses speedup and percentage beats on LeetCode. Multiple metrics enable multifaceted evaluation.", 201 "source": "haiku" 202 }, 203 "human_evaluation": { 204 "applies": true, 205 "answer": false, 206 "justification": "No human participants or human evaluation of code. Efficiency measured automatically via simulator and LeetCode platform submissions.", 207 "source": "haiku" 208 }, 209 "held_out_test_set": { 210 "applies": true, 211 "answer": true, 212 "justification": "HumanEval and MBPP use standard held-out test cases. LeetCodeEval leverages LeetCode's official test suites for correctness and runtime verification.", 213 "source": "haiku" 214 }, 215 "per_category_breakdown": { 216 "applies": true, 217 "answer": true, 218 "justification": "Results broken down by difficulty (easy/medium/hard), model variants (Tables 2-3), prompting methods (Table 4), and language (Python vs C++).", 219 "source": "haiku" 220 }, 221 "failure_cases_discussed": { 222 "applies": true, 223 "answer": false, 224 "justification": "Paper mentions treating failures as speedup=1 but does not discuss which problems failed, why, or patterns in failures across models and prompts.", 225 "source": "haiku" 226 }, 227 "negative_results_reported": { 228 "applies": true, 229 "answer": true, 230 "justification": "Paper reports DeepSeek Coder 33B Base poor speedup (1.00-1.05), Phi-2 slower code in some cases, and hard subset has 0 passing problems (making evaluation impossible).", 231 "source": "haiku" 232 } 233 }, 234 "setup_transparency": { 235 "model_versions_specified": { 236 "applies": true, 237 "answer": true, 238 "justification": "GPT models specified with exact version IDs (gpt-3.5-turbo-1106, gpt-4-1106-preview). Code Llama, WizardCoder, DeepSeek specified with parameter counts and training variant.", 239 "source": "haiku" 240 }, 241 "prompts_provided": { 242 "applies": true, 243 "answer": true, 244 "justification": "Figure 2 shows LeetCodeEval prompt template with placeholders. Figure 3 describes three prompting methods with example structure. Exact optimization prompts for RQ2 not fully shown.", 245 "source": "haiku" 246 }, 247 "hyperparameters_reported": { 248 "applies": true, 249 "answer": false, 250 "justification": "Temperature, top_p, or other sampling parameters not reported. Paper mentions generating k responses (Pass@10 context suggests k≥10) but exact value and sampling settings absent.", 251 "source": "haiku" 252 }, 253 "scaffolding_described": { 254 "applies": false, 255 "answer": false, 256 "justification": "No agentic scaffolding used. Models queried directly with prompts. Not applicable to this study.", 257 "source": "haiku" 258 }, 259 "data_preprocessing_documented": { 260 "applies": true, 261 "answer": true, 262 "justification": "LeetCodeEval preprocessing documented: filter problems with images and more downvotes than upvotes, split by difficulty. Code from Liu et al. used but preprocessing details external.", 263 "source": "haiku" 264 } 265 }, 266 "data_integrity": { 267 "raw_data_available": { 268 "applies": true, 269 "answer": true, 270 "justification": "Paper claims to release 'data and other artifacts' on GitHub. Raw runtime measurements and problem lists likely available though not explicitly confirmed in paper.", 271 "source": "haiku" 272 }, 273 "data_collection_described": { 274 "applies": true, 275 "answer": true, 276 "justification": "Data collection for HumanEval/MBPP via LLM API calls and gem5 simulation described. LeetCodeEval collection via problem selection and platform submission clearly described.", 277 "source": "haiku" 278 }, 279 "recruitment_methods_described": { 280 "applies": false, 281 "answer": false, 282 "justification": "No human participants recruited. Not applicable.", 283 "source": "haiku" 284 }, 285 "data_pipeline_documented": { 286 "applies": true, 287 "answer": true, 288 "justification": "Pipeline clear: generate code → verify correctness → measure runtime (HumanEval/MBPP via gem5, LeetCodeEval via platform) → repeat and average. High-level documentation present.", 289 "source": "haiku" 290 } 291 }, 292 "contamination": { 293 "training_cutoff_stated": { 294 "applies": true, 295 "answer": true, 296 "justification": "LeetCodeEval uses May 2023 problems 'this is the latest GPT-4 knowledge cutoff'. Other models' training cutoffs not explicitly stated, only GPT covered.", 297 "source": "haiku" 298 }, 299 "train_test_overlap_discussed": { 300 "applies": true, 301 "answer": true, 302 "justification": "Data leakage addressed for GPT via problem date cutoff. Paper does not discuss whether HumanEval/MBPP existed before training cutoff or contamination for other models.", 303 "source": "haiku" 304 }, 305 "benchmark_contamination_addressed": { 306 "applies": true, 307 "answer": true, 308 "justification": "LeetCodeEval explicitly avoids contamination by selecting post-cutoff problems. HumanEval/MBPP are standard benchmarks but potential pre-training contamination not discussed.", 309 "source": "haiku" 310 } 311 }, 312 "human_studies": { 313 "pre_registered": { 314 "applies": false, 315 "answer": false, 316 "justification": "No human participants. Not applicable.", 317 "source": "haiku" 318 }, 319 "irb_or_ethics_approval": { 320 "applies": false, 321 "answer": false, 322 "justification": "No human participants. Not applicable.", 323 "source": "haiku" 324 }, 325 "demographics_reported": { 326 "applies": false, 327 "answer": false, 328 "justification": "No human participants. Not applicable.", 329 "source": "haiku" 330 }, 331 "inclusion_exclusion_criteria": { 332 "applies": false, 333 "answer": false, 334 "justification": "No human participants. Not applicable.", 335 "source": "haiku" 336 }, 337 "randomization_described": { 338 "applies": false, 339 "answer": false, 340 "justification": "No human participants. Not applicable.", 341 "source": "haiku" 342 }, 343 "blinding_described": { 344 "applies": false, 345 "answer": false, 346 "justification": "No human participants. Not applicable.", 347 "source": "haiku" 348 }, 349 "attrition_reported": { 350 "applies": false, 351 "answer": false, 352 "justification": "No human participants. Not applicable.", 353 "source": "haiku" 354 } 355 }, 356 "cost_and_practicality": { 357 "inference_cost_reported": { 358 "applies": true, 359 "answer": false, 360 "justification": "No API call costs reported for GPT models. Local model inference cost or latency not documented. Only runtime of generated code measured, not inference latency.", 361 "source": "haiku" 362 }, 363 "compute_budget_stated": { 364 "applies": true, 365 "answer": false, 366 "justification": "Total computational budget (API costs, GPU hours, simulator CPU time) not reported. Scale of evaluation (10 runs × multiple models × hundreds of problems) not quantified in resource terms.", 367 "source": "haiku" 368 } 369 } 370 } 371 }, 372 "claims": [ 373 { 374 "claim": "Code generation ability (correctness) is not positively correlated with code efficiency ability", 375 "evidence": "GPT-4 highest Pass@10 (98.2% HumanEval) but GPT-3.5 generates faster code (8.35 vs 8.61 normalized runtime). Phi-2 lowest Pass@10 (62.8%) but generates fastest or near-fastest code.", 376 "supported": "strong" 377 }, 378 { 379 "claim": "Model parameter size does not determine code efficiency", 380 "evidence": "Code Llama series (7B, 13B, 34B) shows runtime 9.95→9.87→9.93 (stable). WizardCoder similar pattern 9.35→9.18→9.04 without clear scaling.", 381 "supported": "moderate" 382 }, 383 { 384 "claim": "Training strategy and data significantly impact efficiency of generated code", 385 "evidence": "DeepSeek Coder 33B Base vs Instruct: 9.40 vs 7.54 runtime on HumanEval (22% difference from instruction-tuning alone).", 386 "supported": "moderate" 387 }, 388 { 389 "claim": "Chain-of-thought prompting enables more efficient code generation on complex problems", 390 "evidence": "Prompts 2&3 show 1.16-1.18x speedup on LeetCode medium vs Prompt 1 at 1.07x for GPT-4. Effect stronger on harder problems due to larger optimization space.", 391 "supported": "moderate" 392 }, 393 { 394 "claim": "Prompting effectiveness varies by problem complexity and benchmark", 395 "evidence": "LeetCodeEval shows larger speedups (1.03-1.18x) vs HumanEval/MBPP (1.00-1.06x). Medium subset gap wider than easy due to constrained vs large optimization space.", 396 "supported": "moderate" 397 } 398 ], 399 "methodology_tags": [ 400 "benchmark-eval", 401 "observational" 402 ], 403 "key_findings": "The paper demonstrates that code efficiency in LLM-generated code is orthogonal to correctness and model size, driven instead by training strategy. Chain-of-thought prompting yields 3-18% speedups on complex problems, though gains diminish on simple problems. Benchmark choice matters: LeetCode's larger test cases reveal efficiency differences invisible to HumanEval/MBPP.", 404 "red_flags": [ 405 { 406 "flag": "Gem5 simulator validity unvalidated", 407 "detail": "Paper uses gem5 simulator to measure runtime but does not validate correlation with actual wall-clock runtime. Simulator could introduce systematic biases not present in real execution." 408 }, 409 { 410 "flag": "Severe sample attrition in comparisons", 411 "detail": "Only 70/164 HumanEval and 242/399 MBPP problems pass all LLMs (43% and 61% retention). Hard subset has 0 problems passing, making it impossible to evaluate on hardest tasks." 412 }, 413 { 414 "flag": "No statistical significance testing", 415 "detail": "Differences in normalized runtime are reported without p-values or confidence intervals despite small samples and potential runtime variance. Risk of noise being reported as signal." 416 }, 417 { 418 "flag": "Hyperparameters not reported", 419 "detail": "Temperature, top_p, and sampling method not disclosed. These significantly impact output and could explain differences between models or prompts." 420 }, 421 { 422 "flag": "Inconsistent model coverage in prompting study", 423 "description": "RQ1 evaluates 6 models but RQ2 prompting only tests 3 models (GPT-4, GPT-3.5, DeepSeek Coder). No prompting data for Code Llama or WizardCoder variants." 424 }, 425 { 426 "flag": "Limited mechanistic understanding", 427 "detail": "Paper observes that correctness and efficiency decouple but does not investigate why—are models not trained for efficiency? Is this a random variation? Do different architectures handle trade-offs differently?" 428 }, 429 { 430 "flag": "Narrow efficiency definition", 431 "detail": "Only runtime measured. Memory efficiency, code size, maintainability, and readability not addressed despite being relevant efficiency dimensions." 432 } 433 ], 434 "cited_papers": [ 435 { 436 "title": "Program Synthesis with Large Language Models", 437 "relevance": "Foundational work on LLM code generation capabilities and benchmarks" 438 }, 439 { 440 "title": "Evaluating Large Language Models Trained on Code", 441 "relevance": "Introduces HumanEval benchmark and correctness evaluation methodology" 442 }, 443 { 444 "title": "Is Your Code Generated by ChatGPT Really Correct?", 445 "relevance": "Prior work evaluating correctness of LLM code generation across models" 446 }, 447 { 448 "title": "Learning Performance-Improving Code Edits", 449 "relevance": "PIE dataset and chain-of-thought prompting for code optimization (directly cited for Prompt 2&3)" 450 }, 451 { 452 "title": "DeepDev-PERF: a deep learning-based approach for improving software performance", 453 "relevance": "Alternative approach to code efficiency improvement using deep learning" 454 }, 455 { 456 "title": "Code Llama: Open Foundation Models for Code", 457 "relevance": "Description of Code Llama architecture and capabilities, one of evaluated models" 458 }, 459 { 460 "title": "DeepSeek LLM: Scaling Open-Source Language Models with Longtermism", 461 "relevance": "DeepSeek Coder model description and capabilities" 462 }, 463 { 464 "title": "Evaluating the code quality of ai-assisted code generation tools", 465 "relevance": "Prior work on code quality metrics and LLM evaluation beyond correctness" 466 } 467 ], 468 "engagement_factors": { 469 "practical_relevance": { 470 "score": 2, 471 "justification": "Practitioners care about efficiency, but recommendations (use newer models, use chain-of-thought) are limited and context-dependent. Hard subset unevaluable limits real-world applicability." 472 }, 473 "surprise_contrarian": { 474 "score": 2, 475 "justification": "Decoupling of correctness and efficiency is somewhat unexpected, but efficiency not correlating with raw capability is intuitive. No major paradigm shift challenged." 476 }, 477 "fear_safety": { 478 "score": 0, 479 "justification": "No safety concerns, vulnerabilities, or alignment issues raised. Purely performance-oriented study." 480 }, 481 "drama_conflict": { 482 "score": 0, 483 "justification": "Straightforward empirical study with no controversy, competitive comparison drama, or contentious claims." 484 }, 485 "demo_ability": { 486 "score": 2, 487 "justification": "Could demonstrate by running LLM code through LeetCode or simulator, but requires API access and setup. Not immediately reproducible for casual readers." 488 }, 489 "brand_recognition": { 490 "score": 1, 491 "justification": "Nanjing University (moderate tier internationally), Singapore Management University, UT Dallas. FORGE 2024 is a specialized venue, not a top-tier conference. Limited brand visibility." 492 } 493 }, 494 "hn_data": { 495 "threads": [ 496 { 497 "hn_id": "40370779", 498 "title": "Simultaneous Many-Row Activation in Off-the-Shelf DRAM Chips", 499 "points": 7, 500 "comments": 0, 501 "url": "https://news.ycombinator.com/item?id=40370779", 502 "created_at": "2024-05-15T18:44:38Z" 503 }, 504 { 505 "hn_id": "39368490", 506 "title": "Keyframer: Empowering Animation Design Using Large Language Models (Apple)", 507 "points": 6, 508 "comments": 1, 509 "url": "https://news.ycombinator.com/item?id=39368490", 510 "created_at": "2024-02-14T10:48:19Z" 511 }, 512 { 513 "hn_id": "40286055", 514 "title": "Forklift: An Extensible Neural Lifter", 515 "points": 3, 516 "comments": 0, 517 "url": "https://news.ycombinator.com/item?id=40286055", 518 "created_at": "2024-05-07T14:39:26Z" 519 }, 520 { 521 "hn_id": "43426799", 522 "title": "Aardvark weather: end-to-end data-driven weather forecasting", 523 "points": 2, 524 "comments": 0, 525 "url": "https://news.ycombinator.com/item?id=43426799", 526 "created_at": "2025-03-20T18:10:12Z" 527 }, 528 { 529 "hn_id": "43211832", 530 "title": "Heat as a Witness of Quantum Properties", 531 "points": 2, 532 "comments": 0, 533 "url": "https://news.ycombinator.com/item?id=43211832", 534 "created_at": "2025-02-28T21:48:33Z" 535 }, 536 { 537 "hn_id": "41245268", 538 "title": "Dwellers in the Deep: Biological Consequences of Dark Oxygen", 539 "points": 2, 540 "comments": 0, 541 "url": "https://news.ycombinator.com/item?id=41245268", 542 "created_at": "2024-08-14T12:25:02Z" 543 }, 544 { 545 "hn_id": "40948891", 546 "title": "Fast-moving stars around an intermediate-mass black hole in Omega Centauri", 547 "points": 2, 548 "comments": 0, 549 "url": "https://news.ycombinator.com/item?id=40948891", 550 "created_at": "2024-07-12T20:03:03Z" 551 }, 552 { 553 "hn_id": "39050109", 554 "title": "Mission: Impossible Language Models", 555 "points": 2, 556 "comments": 0, 557 "url": "https://news.ycombinator.com/item?id=39050109", 558 "created_at": "2024-01-19T00:38:50Z" 559 }, 560 { 561 "hn_id": "39026660", 562 "title": "Mission: Impossible Language Models", 563 "points": 2, 564 "comments": 0, 565 "url": "https://news.ycombinator.com/item?id=39026660", 566 "created_at": "2024-01-17T12:11:54Z" 567 }, 568 { 569 "hn_id": "41284222", 570 "title": "Assessing the Learning Limits of LLMs with Synthetic Impossible Languages", 571 "points": 1, 572 "comments": 0, 573 "url": "https://news.ycombinator.com/item?id=41284222", 574 "created_at": "2024-08-18T18:27:15Z" 575 } 576 ], 577 "top_points": 7, 578 "total_points": 29, 579 "total_comments": 1 580 } 581 }