Papers by Andrew Gambardella
Which Programming Language and What Features at Pre-training Stage Affect Downstream Logical Inference Performance? (2024.emnlp-main)
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| Challenge: | Recent large language models (LLMs) have demonstrated remarkable generalization abilities in mathematics and reasoning tasks. |
| Approach: | They pre-trained decoder-based language models from scratch using ten programming languages and three natural language datasets. |
| Outcome: | The proposed models outperform natural languages on logical reasoning tasks. |
Language Models Do Hard Arithmetic Tasks Easily and Hardly Do Easy Arithmetic Tasks (2024.acl-short)
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| Challenge: | Despite the generality and far-reaching consequences of large language models, there are still significant limitations making it difficult to apply them to certain tasks. |
| Approach: | They show that large language models can perform arithmetic tasks more robustly when conditioned on all of the correct higher-order digits. |
| Outcome: | The proposed model can predict the first digit of n-digit by m-digit multiplication without chain of thought reasoning, but in practice it fails to correctly predict the last digit on n digit by 1-digit multiplikation . |
Inconsistent Tokenizations Cause Language Models to be Perplexed by Japanese Grammar (2025.acl-short)
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| Challenge: | Standard benchmarks for language models fail to capture nuanced capabilities such as the ability of language models to recognize and obey rare grammar points. |
| Approach: | They find that Weblab's uniformly bad tokenization is a possible root cause for its good performance . |
| Outcome: | The proposed model consistently assigns higher perplexity to ungrammatical psych predicate sentences than grammaticals. |
Answer When Needed, Forget When Not: Language Models Pretend to Forget via In-Context Knowledge Unlearning (2025.findings-acl)
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| Challenge: | Large language models (LLMs) are increasingly required to selectively unlearn specific information. |
| Approach: | They propose a method which fine-tunes pre-trained LLMs to enable prompt unlearning of target knowledge within the context while preserving unrelated information. |
| Outcome: | The proposed method achieves up to 95% forget accuracy while retaining 80% of unrelated knowledge, significantly outperforming baselines in both in-domain and out-of-domain scenarios. |
Clustered Self-Assessment: A Simple yet Effective Method for Uncertainty Quantification in Large Language Models (2026.findings-acl)
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| Challenge: | Existing methods for uncertainty quantification in large language models rely on indirect signals, such as entropy across sampled generations, which can be difficult to interpret and do not fully leverage the model’s ability to assess its own uncertainty. |
| Approach: | They propose a method that groups sampled generations into semantically distinct clusters and uses the probability assigned by the LLM to each option as a confidence estimate. |
| Outcome: | The proposed method outperforms baseline methods and achieves competitive performance with as few as two additional samples. |
Semantic Token Clustering for Efficient Uncertainty Quantification in Large Language Models (2026.eacl-short)
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| Challenge: | Large language models have limited truthfulness and tendency toward overconfidence constrain reliability in factual tasks. |
| Approach: | They propose an efficient method that leverages semantic information encoded in LLMs to quantify uncertainty. |
| Outcome: | The proposed method achieves comparable performance to baselines while significantly reducing computational overhead. |