Papers by Anhad Mohananey
BLiMP: The Benchmark of Linguistic Minimal Pairs for English (2020.tacl-1)
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Alex Warstadt, Alicia Parrish, Haokun Liu, Anhad Mohananey, Wei Peng, Sheng-Fu Wang, Samuel R. Bowman
| Challenge: | Recent studies have examined how linguistic knowledge of language models (LMs) varies across English phenomena. |
| Approach: | They propose a benchmark to evaluate linguistic knowledge of language models on major grammatical phenomena in English. |
| Outcome: | The proposed benchmark evaluates the linguistic knowledge of language models on major grammatical phenomena in English. |
Investigating BERT’s Knowledge of Language: Five Analysis Methods with NPIs (D19-1)
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Alex Warstadt, Yu Cao, Ioana Grosu, Wei Peng, Hagen Blix, Yining Nie, Anna Alsop, Shikha Bordia, Haokun Liu, Alicia Parrish, Sheng-Fu Wang, Jason Phang, Anhad Mohananey, Phu Mon Htut, Paloma Jeretic, Samuel R. Bowman
| Challenge: | Recent work evaluating sentence representation models' knowledge of grammar has been slower to emerge. |
| Approach: | They propose five experimental methods inspired by prior work evaluating pretrained sentence representation models to examine their grammatical knowledge. |
| Outcome: | The proposed methods show that the model has significant knowledge of the licensing environment but its success varies widely across different methods. |
Neural Unsupervised Parsing Beyond English (D19-61)
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| Challenge: | Unsupervised parsing is a task that can be learned without substantial prior knowledge. |
| Approach: | They train an unsupervised model for Arabic, Chinese, English, and German to learn syntactic structure from unlabeled text. |
| Outcome: | The PRPN architecture outperforms trivial baselines and acquires at least some parsing ability for all languages. |
Fact, Fetch, and Reason: A Unified Evaluation of Retrieval-Augmented Generation (2025.naacl-long)
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Satyapriya Krishna, Kalpesh Krishna, Anhad Mohananey, Steven Schwarcz, Adam Stambler, Shyam Upadhyay, Manaal Faruqui
| Challenge: | Recent advances in Large Language Models (LLMs) have significantly enhanced their capabilities across various cognitive tasks. |
| Approach: | They propose a high-quality evaluation dataset to test LLMs' ability to provide factual responses, assess retrieval capabilities, and evaluate the reasoning required to generate final answers. |
| Outcome: | The proposed framework improves performance in end-to-end RAG scenarios. |
Do LLMs Really Need 10+ Thoughts for “Find the Time 1000 Days Later”? Towards Structural Understanding of LLM Overthinking (2026.acl-long)
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Xinliang Frederick Zhang, Anhad Mohananey, Alexandra Chronopoulou, Pinelopi Papalampidi, Somit Gupta, Tsendsuren Munkhdalai, Lu Wang, Shyam Upadhyay
| Challenge: | Existing studies on LLMs' thought processes are limited to superficial, profiling-based observations, failing to delve into their inner workings. |
| Approach: | They propose a utility-based definition of overthinking that moves beyond length-based metrics and provides a more insightful understanding of LLMs' thought progression. |
| Outcome: | The proposed model decomposes the LLM thought process into minimally complete sub-thoughts and identifies common thinking patterns for topically similar queries. |