Papers by Lisa Alazraki
No Need for Explanations: LLMs can implicitly learn from mistakes in-context (2025.emnlp-main)
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| Challenge: | Existing literature assumes that correct answers to large language models must be accompanied by comprehensive rationales to be helpful. |
| Approach: | They propose to show incorrect answers to Large Language Models (LLMs) as a popular strategy to improve their performance in reasoning-intensive tasks. |
| Outcome: | The proposed approach outperforms chain-of-thought prompting in math reasoning tasks. |
Improving the OOD Performance of Closed-Source LLMs on NLI Through Strategic Data Selection (2026.findings-eacl)
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| Challenge: | Existing methods to improve robustness require changing the fine-tuning process or large-scale data augmentation, which are infeasible or cost prohibitive for closed-source models. |
| Approach: | They propose to prioritize more complex examples or replace existing training examples with LLM-generated data to improve performance on OOD NLI datasets. |
| Outcome: | The proposed methods improve performance on difficult OOD datasets while training with synthetic data leads to substantial improvements on easier OOD data. |
Meta-Reasoning Improves Tool Use in Large Language Models (2025.findings-naacl)
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| Challenge: | External tools have proven effective at boosting the performance of large language models in knowledge-intensive tasks such as QA and math problem-solving. |
| Approach: | They propose a tool-based meta-reasONing system that makes a final decision based on a custom language modelling head. |
| Outcome: | The proposed system improves the performance of large language models in QA and math problem-solving tasks. |
AgentCoMa: A Compositional Benchmark Mixing Commonsense and Mathematical Reasoning in Real-World Scenarios (2026.acl-long)
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| Challenge: | brittleness of Large Language Models in reasoningintensive tasks is a problem . current compositional benchmarks focus on *either* commonsense or math reasoning . |
| Approach: | They propose a "Co**mmonsense and Ma**th" benchmark where each compositional task requires a commonsense reasoning step *and* a math reasoning step. |
| Outcome: | The proposed benchmarks show that LLMs can solve both steps in isolation, but their accuracy drops by nearly 30% when the two steps are combined. |