Papers with out-of-distribution settings
ReLM: Leveraging Language Models for Enhanced Chemical Reaction Prediction (2023.findings-emnlp)
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| Challenge: | Existing methods for predicting chemical reactions are limited by insufficient training data and inability to utilize textual information. |
| Approach: | They propose a framework that leverages chemical knowledge encoded in language models to assist GNNs, thereby enhancing the accuracy of real-world chemical reaction predictions. |
| Outcome: | The proposed framework improves state-of-the-art GNN-based methods across chemical reaction datasets especially in out-of distribution settings. |
Exploiting Tree Structure for Credit Assignment in Reinforcement Learning with Large Language Models (2026.findings-acl)
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| Challenge: | Reinforcement learning has shown strong promise for strengthening reasoning ability of large language models, but sparse, delayed rewards make token-level credit assignment a central challenge. |
| Approach: | They propose a critic-free algorithm that rewards tokens that change the solution. |
| Outcome: | The proposed algorithm improves on in-distribution benchmarks and out-of-disttribution settings. |
CRIPP-VQA: Counterfactual Reasoning about Implicit Physical Properties via Video Question Answering (2022.emnlp-main)
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| Challenge: | Videos often capture objects, their visible properties, their motion, and the interactions between different objects. |
| Approach: | They propose a video question answering dataset for reasoning about the implicit physical properties of objects in a scene. |
| Outcome: | The proposed dataset enables evaluation under several out-of-distribution settings – videos with objects with masses, coefficients of friction, and initial velocities that are not observed in the training distribution. |