Papers by Raul Santos-Rodriguez
Teaching Language Models to Check Grounded Claim Factuality with Human Test-Taking Strategies (2026.acl-long)
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| Challenge: | Existing methods for factuality checking require dataset-specific threshold tuning, while LLM-based approaches often use direct prompting. |
| Approach: | They propose to use a reading comprehension task to check for true/false claim factuality and prompt LLMs with explicit test-taking strategies for efficient reasoning. |
| Outcome: | The proposed method reduces token usage by over 80% compared to unguided open-ended reasoning and achieves competitive performance to more expensive alternatives. |
Optimising Factual Consistency in Summarisation via Preference Learning from Multiple Imperfect Metrics (2025.findings-emnlp)
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| Challenge: | Existing evaluation metrics are unreliable for factual consistency tasks, limiting their effectiveness as signals for shaping model behaviour. |
| Approach: | They propose an automated training pipeline that improves factual consistency in summaries by aggregating scores from different weak metrics. |
| Outcome: | The proposed approach improves factual consistency in summaries by aggregating scores from weak metrics. |