Papers with transformer-based LMs
LM-Debugger: An Interactive Tool for Inspection and Intervention in Transformer-Based Language Models (2022.emnlp-demos)
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| Challenge: | Transformer-based language models (LMs) are opaque and unexplained, causing problems for endusers and developers who wish to debug or fix their behaviour. |
| Approach: | They propose an interactive debugger tool for transformer-based LMs that provides a fine-grained interpretation of the model's internal prediction process and a powerful framework for intervening in LM behavior. |
| Outcome: | The proposed tool provides a fine-grained interpretation of the model's internal prediction construction process, and a powerful framework for intervening in LM behavior. |
Invoke Interfaces Only When Needed: Adaptive Invocation for Large Language Models in Question Answering (2025.findings-emnlp)
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| Challenge: | a new metric is developed to pinpoint the moment of invocation when hallucinations arise in small LMs. |
| Approach: | They propose a metric that measures hallucinations during the generation process of small LMs. |
| Outcome: | The proposed metric outperforms baselines in hallucination detection across multiple QA datasets. |
Scalable Fine-tuning from Multiple Data Sources: A First-Order Approximation Approach (2024.findings-emnlp)
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| Challenge: | a new algorithm to estimate fine-tuning performance for a target task is proposed . conventional subset selection methods require repeated training on subsets of auxiliary tasks . |
| Approach: | They propose an algorithm to fine-tune a language model for a target task by optimally using auxiliary tasks' information. |
| Outcome: | The proposed method can estimate fine-tuning performance on CPUs in seconds. |