Papers with in-context learning models
C-STS: Conditional Semantic Textual Similarity (2023.emnlp-main)
Copied to clipboard
Ameet Deshpande, Carlos Jimenez, Howard Chen, Vishvak Murahari, Victoria Graf, Tanmay Rajpurohit, Ashwin Kalyan, Danqi Chen, Karthik Narasimhan
| Challenge: | Semantic textual similarity (STS) is a cornerstone task in natural language processing, but it is inherently ambiguous. |
| Approach: | They propose a task called conditional STS which measures similarity conditioned on an aspect elucidated in natural language. |
| Outcome: | The proposed task reduces subjectivity and ambiguity and enables fine-grained similarity evaluation using diverse conditions. |
Question Answering over Tabular Data with DataBench: A Large-Scale Empirical Evaluation of LLMs (2024.lrec-main)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) are showing emerging abilities, but they are not large enough to assess their capabilities. |
| Approach: | They propose a benchmark that compares large language models with open and closed source models. |
| Outcome: | The proposed benchmark compares open and closed-source models with open-source and closed source models. |
Automatic Combination of Sample Selection Strategies for Few-Shot Learning (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing studies on small language models are characterised by a labelled data scarcity due to data collection/annotation costs or privacy considerations, making the training of typical deep learning models unfeasible. |
| Approach: | They propose a method for Automatic Combination of SamplE Selection Strategies to leverage the strengths and complementarity of various well-established selection objectives. |
| Outcome: | The proposed method outperforms all in-context learning strategies and performs on par or exceeds the in-constinction learning specific baselines. |