Harder Benchmarks Are Not Always Better: Evaluating LLM Distractors for Multiple-Choice Benchmarks Atrey Desai, Nishant Balepur, Rachel Rudinger Under ACL ARR Review, 2026 preprint tldr pdf NLPEvaluation What best describes skin?ABCDflexiblewho writes thewrong answers?stiffbrittlehardhumans write themor LLMs createwrong answersscored on three metricsdifficultyGPT-5.2 on MMLU: difficulty above human-guidedGPT-5.2 on ARC: difficulty above human-guidedGemini-3.1 Pro on MMLU: difficulty above human-guidedGemini-3.1 Pro on ARC: difficulty above human-guidedQwen-3.5 397B on MMLU: difficulty level with human-guidedGPT-5.2 on GPQA: difficulty above human-guidedQwen-3.5 397B on ARC: difficulty above human-guidedGemini-3.1 Pro on GPQA: difficulty above human-guidedQwen-3.5 397B on GPQA: difficulty below human-guideddiscriminabilityGPT-5.2 on MMLU: discriminability above human-guidedGPT-5.2 on ARC: discriminability above human-guidedGemini-3.1 Pro on MMLU: discriminability above human-guidedGemini-3.1 Pro on ARC: discriminability above human-guidedQwen-3.5 397B on MMLU: discriminability above human-guidedGPT-5.2 on GPQA: discriminability below human-guidedQwen-3.5 397B on ARC: discriminability below human-guidedGemini-3.1 Pro on GPQA: discriminability below human-guidedQwen-3.5 397B on GPQA: discriminability below human-guidedwriting qualityGPT-5.2 on MMLU: writing quality below human-guidedGPT-5.2 on ARC: writing quality below human-guidedGemini-3.1 Pro on MMLU: writing quality above human-guidedGemini-3.1 Pro on ARC: writing quality above human-guidedQwen-3.5 397B on MMLU: writing quality above human-guidedGPT-5.2 on GPQA: writing quality below human-guidedQwen-3.5 397B on ARC: writing quality below human-guidedGemini-3.1 Pro on GPQA: writing quality above human-guidedQwen-3.5 397B on GPQA: writing quality above human-guided← worsehumanbetter →LLM distractorshave trade-offs+LLMs scaleMCQA benchmarks.Humans canhelp monitor.
Test-Time Reasoners Are Strategic Multiple-Choice Test-Takers Nishant Balepur, Atrey Desai, Rachel Rudinger ACL Main, 2026 tldr arxiv pdf code NLPReasoning ? ? ?which of the followingis a renewable resource?AoilfiniteBcoalfiniteCtreesrenewableDaluminumfiniteanswer it withoutthe question?naming propertiesof choicesinferring theoriginal questionchoices-only accuracyARC, without the question25%random56%GPT-5?Not always a flaw.Reasoning tracesreveal strategies.
Asking About Occluded Content Marks Adversarial Multimodal Questions HyoJung Han*, Atrey Desai*, Ishan Upadhyay, Tianyi Zheng, Eve Fleisig, Aparna Garimella, Marine Carpuat, Jordan Boyd-Graber Under ACL ARR Review, 2026 preprint tldr pdf NLPMultimodalEvaluation
Filling in the Mechanisms: How do LMs Learn Filler-Gap Dependencies under Developmental Constraints? Atrey Desai, Sathvik Nair ACL Findings; TLS, 2026 Oral (TLS) arxiv pdf code NLPLinguistics Whodid the teacher like?fillergaplearnable fromchildren’s input?Did the teacher likehimnext word?fillerinject the learnedfiller-gap featurecausal effect1M10M100Mstrongkids ✓age 2–5LM: weakLMincreasing withtraining tokensIt emerges,but still requiresfar more data thanchildren would.
BenchMarker: An Education-Inspired Toolkit for Highlighting Flaws in Multiple-Choice Benchmarks Nishant Balepur, Bhavya Rajasekaran, …, Atrey Desai, … ACL Main, 2026 arxiv pdf code NLPEvaluation LLM judges flag 3 flawsfound online?has a shortcut?writing errors?a report cardfor benchmarksfound online2+ writing errorsAQuA: 17% found online, 44% violate 2+ writing rulesARC: 39% found online, 43% violate 2+ writing rulesMMLU: 45% found online, 64% violate 2+ writing rulesSAT: 24% found online, 25% violate 2+ writing rulesCommonsenseQA: 6% found online, 96% violate 2+ writing rulesHellaSwag: 0% found online, 100% violate 2+ writing rulesOpenBookQA: 4% found online, 90% violate 2+ writing rulesPIQA: 5% found online, 93% violate 2+ writing rulesQASC: 6% found online, 97% violate 2+ writing rulesSocialIQA: 0% found online, 95% violate 2+ writing rulesSuperGPQA: 6% found online, 77% violate 2+ writing rulesTruthfulQA: 47% found online, 97% violate 2+ writing rules45%100%studentnon-studentexam MCQs aremore contaminatedexams have farfewer writing flawsLLM rankingsall items2+ writing errors removed1234GPT-5Claude 4.5 SonnetGemini 2.5 ProGPT-5 Miniwriting errorschange rankings
A Preview of Computational Animal Linguistics Tuan M. Dang, Atrey Desai, Hridayesh Lekhak, Tirza Panunto, Lindsay Pike, Theron S. Wang, Kenny Q. Zhu ACM Computing Surveys, 2026 pdf NLPLinguistics 13 linguistic propertiesIsignalturn-takingIItokenreferenceIIImessagesyntaxIVlanguageopennesspreviously thoughtunique to humansrudimentary tomost complex1data collection2preprocessing3sequence representation4meaning identification5generationevaluationa 5-step pipeline55,843 papers1950–2026biological+ computational≤1979: about 0.3k computational papers1980s: about 0.5k computational papers1990s: about 1.1k computational papers2000s: about 2.8k computational papers2010s: about 6.0k computational papers2020–26: about 7.4k computational papers≤1979: about 2.1k biological papers1980s: about 2.4k biological papers1990s: about 3.6k biological papers2000s: about 7.2k biological papers2010s: about 12.2k biological papers2020–26: about 10.3k biological papers≤7980s90s00s10s20sthe recentaccelerationevidenceaccuracyobserver-sideclassificationanimal-siderequires receiver behavioraccuracy cannotby itself establishanimal-side claims
Language Models Generate Multiple-Choice Questions with Artifacts Atrey Desai, Nishant Balepur, Rachel Rudinger MASC-SLL, 2025 pdf NLPEvaluation logic translation of:choices only:‘For all x, if P of xthen Q of x’A∃x (P(x) ∧ Q(x))B∀x (P(x) ∧ Q(x))C∀x (P(x) → Q(x))D∃x (P(x) → Q(x))an LLM wrote thisstill solvedGPT-4o-mini accuracyhuman-writtenLLM-generatedwith80%no Q38%with96%no Q96%often exceeds 90%using only choicesLLMs’ questionslook good at first…… but they’re fullof shortcuts!
Reinforcement Learning As End-User Trigger-Action Programming Chace Hayhurst, Hyojae Park, …, Atrey Desai, … AAAI IML Workshop 2022, RLDM 2022, 2022 pdf RLHCI diningcan end usersprogram a robot?trigger-action rulesif entry: go northif kitchen: go northRL: name the goalgoal: dining room?Consider whatan agent’s objective is,instead of how it canultimately be achieved.
From LOL to LLM: Measuring Multilingual Multi-Turn Humor Understanding in AI Atrey Desai, Leo Du, James van Doorn, Kamala Sreepada tldr pdf NLPMultilingual stand-up, line by linecontextcontexttimingcontextpunchlineescalationcan an LLM findthe punchline?calls the punchline“escalation”OLMo 3.1 32B, punchlines97%missedthe label incorrectlyguessed the mostjoke classificationEnglish subset, Pass@5OLMo 3 7B: 0.0%Apertus 8B: 3.1%Ministral 8B: 0.0%Qwen3 8B: 4.5%Falcon3 10B: 8.0%OLMo 3.1 32B: 26.6%Qwen3 32B: 30.8%27%31%7–10B32Bthe 32B modelsclearly outperform
Longitudinal Phonetic Adaptation in YouTube BookTube Creators Atrey Desai tldr pdf Linguistics vocal fryearlylateengagementearlylateabookolive, early videos, 29.6% vocal fryBooksandLala, early videos, 29.5% vocal fryEmma Snyder, early videos, 20% vocal frygabbyreads, early videos, 6.4% vocal fryHailey in Bookland, early videos, 14.8% vocal fryHaley Pham, early videos, 8.5% vocal fryKatytastic, early videos, 14.3% vocal fryonceuponanemilyreads, early videos, 10.2% vocal frySara Carrolli, early videos, 8% vocal frySyd BookWorrom, early videos, 8.7% vocal fryabookolive, early videos, engagement rate 5BooksandLala, early videos, engagement rate 4.7Emma Snyder, early videos, engagement rate 3.6gabbyreads, early videos, engagement rate 4Hailey in Bookland, early videos, engagement rate 3.1Haley Pham, early videos, engagement rate 4Katytastic, early videos, engagement rate 2.7onceuponanemilyreads, early videos, engagement rate 6.5Sara Carrolli, early videos, engagement rate 4.1Syd BookWorrom, early videos, engagement rate 6.1abookolive, late videos, 9.8% vocal fryBooksandLala, late videos, 14.4% vocal fryEmma Snyder, late videos, 11.2% vocal frygabbyreads, late videos, 3.9% vocal fryHailey in Bookland, late videos, 8.3% vocal fryHaley Pham, late videos, 5.2% vocal fryKatytastic, late videos, 12.4% vocal fryonceuponanemilyreads, late videos, 10.7% vocal frySara Carrolli, late videos, 2% vocal frySyd BookWorrom, late videos, 9.1% vocal fryabookolive, late videos, engagement rate 10.5BooksandLala, late videos, engagement rate 7.4Emma Snyder, late videos, engagement rate 5.1gabbyreads, late videos, engagement rate 7.1Hailey in Bookland, late videos, engagement rate 6.5Haley Pham, late videos, engagement rate 4.3Katytastic, late videos, engagement rate 9.2onceuponanemilyreads, late videos, engagement rate 7.2Sara Carrolli, late videos, engagement rate 6.3Syd BookWorrom, late videos, engagement rate 9.4ten BookTubers’earliest uploadsvocal fry decreasedengagement roseOn averagevocal fry (%)15.0→8.7pitch range (Hz)290.9→319.3engagement rate4.38→7.31Instead of becomingmore informal,creators tend toprofessionalize.
Filler-Gap Dependencies under Developmental Constraints in LMs The University of Texas at Austin, Feb 2026
Adaptor Grammars and Neural Networks for Feline Lexical Discovery University of Maryland, College Park, Nov 2024 The University of Texas at Arlington, Jul 2024
Harder, but Are They Better? Measuring the Quality of LLM-Generated MCQ Distractors University of Maryland, College Park, May 2026
Language Models Generate Multiple-Choice Questions with Artifacts University of Maryland, College Park, Jul 2025 University of Maryland, College Park, Apr 2025