Papers by Pang Koh

2 papers
OLMoTrace: Tracing Language Model Outputs Back to Trillions of Training Tokens (2025.acl-demo)

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Challenge: tracing language models' outputs back to training data is a problem because they are trained on text corpora with trillions of tokens . existing methods for tracers have not been scaled to work within this multi-trillion-token setting .
Approach: They propose a system that traces language models' outputs verbatim back to training data . OLMOTRACE retrieves documents from the model's training data that contain exact matches .
Outcome: The proposed system can find verbatim matches between LM output and training data . it can be used to explore fact checking, hallucination, and creativity of language models .
FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation (2023.emnlp-main)

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Challenge: Evaluating the factuality of long-form text generated by large language models (LMs) is non-trivial because (1) generations often contain a mixture of supported and unsupported pieces of information, making binary judgments of quality inadequate and (2) human evaluation is time-consuming and costly.
Approach: They introduce a new evaluation that breaks a generation into a series of atomic facts and computes the percentage of atom facts supported by a reliable knowledge source.
Outcome: The proposed model breaks a generation into atomic facts and computes the percentage of atomic fact supported by a reliable knowledge source.

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