Papers by Swarnadeep Bhar
SSA: Improving Performance With a Better Scoring Function (2026.acl-long)
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| Challenge: | Despite the success of in-context learning, recent studies have identified systematic limitations in its generalization behavior. |
| Approach: | They propose a new attention scoring function that mitigates failures in transformer models . they use Scaled Signed Averaging to train the scoring function instead of Softmax . |
| Outcome: | The proposed scoring function outperforms transformer models with Softmax on NLP benchmarks and linguistic probing tasks. |
Limits for learning with language models (2023.starsem-1)
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| Challenge: | Recent studies show that large language models fail to capture important aspects of linguistic meaning . authors argue that LLMs cannot learn fundamental semantic properties defined in formal semantics . |
| Approach: | They propose a theoretical explanation for some of the observed failings of large language models . they show that LLMs cannot learn certain fundamental semantic properties . |
| Outcome: | The proposed model fails to learn semantic entailment and consistency as defined in formal semantics, the authors argue . their model fails on tasks that require engorgements and deep linguistic understanding, they argue - but not on universal quantification. |
Strong hallucinations from negation and how to fix them (2024.findings-acl)
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| Challenge: | Despite great performance on many tasks, language models still struggle with reasoning, sometimes providing responses that cannot possibly be true because they stem from logical incoherence. |
| Approach: | They propose a way to treat negation as an operation over latent representations that constrains how they may evolve. |
| Outcome: | The proposed approach improves model performance in cloze prompting and natural language inference tasks without training on sparse negative data. |