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Found 12 papers, 9 papers with code
HyperDAS: Towards Automating Mechanistic Interpretability with Hypernetworks
Mechanistic interpretability has made great strides in identifying neural network features (e. g., directions in hidden activation space) that mediate concepts(e. g., the birth year of a person) and enable predictable manipulation.
Querying Databases with Function Calling
While Function Calling is the most common method for interfacing external tools to LLMs, its application to database querying as a tool has been underexplored.
Anchored Preference Optimization and Contrastive Revisions: Addressing Underspecification in Alignment
We study this and find that (i) preference data gives a better learning signal when the underlying responses are contrastive, and (ii) alignment objectives lead to better performance when they specify more control over the model during training.
Updating CLIP to Prefer Descriptions Over Captions
This model correlates with the judgements of blind and low-vision people while preserving transfer capabilities and has interpretable structure that sheds light on the caption--description distinction.
In-Context Learning for Extreme Multi-Label Classification
Multi-label classification problems with thousands of classes are hard to solve with in-context learning alone, as language models (LMs) might lack prior knowledge about the precise classes or how to assign them, and it is generally infeasible to demonstrate every class in a prompt.
Flexible Model Interpretability through Natural Language Model Editing
Model interpretability and model editing are crucial goals in the age of large language models.
CAW-coref: Conjunction-Aware Word-level Coreference Resolution
State-of-the-art coreference resolutions systems depend on multiple LLM calls per document and are thus prohibitively expensive for many use cases (e. g., information extraction with large corpora).
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Rigorously Assessing Natural Language Explanations of Neurons
Natural language is an appealing medium for explaining how large language models process and store information, but evaluating the faithfulness of such explanations is challenging.
BioDEX: Large-Scale Biomedical Adverse Drug Event Extraction for Real-World Pharmacovigilance
We introduce BioDEX, a large-scale resource for Biomedical adverse Drug Event Extraction, rooted in the historical output of drug safety reporting in the U. S. BioDEX consists of 65k abstracts and 19k full-text biomedical papers with 256k associated document-level safety reports created by medical experts.
Causal Proxy Models for Concept-Based Model Explanations
The core of our proposal is the Causal Proxy Model (CPM).
CEBaB: Estimating the Causal Effects of Real-World Concepts on NLP Model Behavior
We introduce CEBaB, a new benchmark dataset for assessing concept-based explanation methods in Natural Language Processing (NLP).
Frozen Pretrained Transformers for Neural Sign Language Translation
Our results show that pretrained language models can be used to improve sign language translation performance and that the self-attention patterns in BERT transfer in zero-shot to the encoder and decoder of sign language translation models.
