Papers with representational analyses
Linking artificial and human neural representations of language (D19-1)
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| Challenge: | a pre-trained BERT architecture is used to fine-tune sentence encoding models on a variety of natural language understanding (NLU) tasks. |
| Approach: | They compare sentence encoding models with fMRI-based fMR predictions of the sentence . they use a pre-trained BERT architecture as a baseline and fine-tune it on a variety of natural language understanding (NLU) tasks. |
| Outcome: | The proposed model does not yield significant improvements in brain decoding performance on the natural language understanding (NLU) tasks. |
Large Reasoning Models Are (Not Yet) Multilingual Latent Reasoners (2026.findings-acl)
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| Challenge: | Recent work shows that large reasoning models arrive at the correct answer before completing textual reasoning steps, indicating the presence of latent reasoning. |
| Approach: | They conduct a systematic investigation of multilingual latent reasoning in large reasoning models across 11 languages. |
| Outcome: | The proposed model arrive at the correct answer before completing the reasoning steps, indicating the presence of latent reasoning. |
When Models Decide and When They Bind: A Two-Stage Computation for Multiple-Choice Question Answering (2026.findings-acl)
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| Challenge: | Multiple-choice question answering (MCQA) is easy to evaluate but adds a meta-task . prior work has shown that language models exhibit selection biases for particular option identifiers such as the label "A" |
| Approach: | They find that option-boundary residual states contain strong linearly decodable signals . winning content position becomes decoded after final option is processed . |
| Outcome: | The proposed model solves the problem and outputs the symbol that represents the answer. |