Papers by Francesco Maria Molfese
Exploring Fine-Tuning for In-Context Retrieval and Efficient KV-Caching in Long-Context Language Models (2026.eacl-short)
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| Challenge: | Long-Context Language Models (LCLMs) can encode entire document collections, offering a strong alternative to retrieval-augmented generation (RAG). |
| Approach: | They propose to use LCLMs to encode documents with context windows of millions of tokens to improve their performance. |
| Outcome: | The proposed training strategies improve long-context performance and their robustness under compression techniques. |
ReTraceQA: Evaluating Reasoning Traces of Small Language Models in Commonsense Question Answering (2026.acl-long)
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| Challenge: | Recent work in language modeling has led to effective SLMs with impressive performance levels across various benchmarks. |
| Approach: | They propose a benchmark that introduces process-level evaluation for commonsense reasoning tasks. |
| Outcome: | The proposed benchmarks show that large language models provide correct answers despite flawed reasoning processes in a substantial portion of cases. |
Right Answer, Wrong Score: Uncovering the Inconsistencies of LLM Evaluation in Multiple-Choice Question Answering (2025.findings-acl)
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| Challenge: | Multiple-choice question answering tasks are one of the most commonly used tasks for evaluating Large Language Models (LLMs). |
| Approach: | They analyze whether existing answer extraction methods are aligned with human judgment and how they are influenced by answer constraints in the prompt across different domains. |
| Outcome: | The proposed evaluation strategies can be inconsistent with human judgment, and can lead to inaccurate and misleading comparisons. |