Complementary Explanations for Effective In-Context Learning (2023.findings-acl)
Copied to clipboard
| Challenge: | Large language models (LLMs) have remarkable capabilities in learning from expla- nations in prompts, but there has been limited understanding of exactly how these explana- tions function or why they are effective. |
| Approach: | They propose a maximal marginal relevance-based exemplar selection approach to construct exemplar sets that are both relevant and comple- mentary. |
| Outcome: | The proposed model improves in- context learning performance across three tasks on multiple LLMs. |
Similar Papers
Can language models learn from explanations in context? (2022.findings-emnlp)
Copied to clipboard
Andrew Lampinen, Ishita Dasgupta, Stephanie Chan, Kory Mathewson, Mh Tessler, Antonia Creswell, James McClelland, Jane Wang, Felix Hill
| Challenge: | Language Models can adapt to a few in-context examples, but without training. |
| Approach: | They examine how explanations of few-shot examples can help Language Models (LMs) explanations can improve performance even without tuning, they find . |
| Outcome: | The proposed explanations outperform hand-tuned explanations on small validation sets. |
Using Natural Language Explanations to Improve Robustness of In-context Learning (2024.acl-long)
Copied to clipboard
| Challenge: | Recent studies show that large language models excel in many tasks via in-context learning (ICL). However, ICL struggles to execute complex tasks such as arithmetic, commonsense, and symbolic reasoning. |
| Approach: | They propose to augment ICL with natural language explanations (NLEs) to produce further NLEs on adversarial datasets. |
| Outcome: | The proposed approach yields more accurate results than zero-shot-ICL and using only human-generated NLEs on eight adversarial datasets. |
MAGNIFICo: Evaluating the In-Context Learning Ability of Large Language Models to Generalize to Novel Interpretations (2023.emnlp-main)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have a knowledge cutoff and are costly to finetune repeatedly. |
| Approach: | They introduce a language evaluation suite that incorporates diverse tokens and prompt settings to simulate real-world complexity. |
| Outcome: | The proposed evaluation suite incorporates diverse tokens and prompt settings to simulate real-world complexity. |
Out-of-Context Reasoning in Large Language Models (2025.findings-emnlp)
Copied to clipboard
| Challenge: | a lightweight technique trains only new token embeddings on axioms and evaluates them on unseen tasks. |
| Approach: | They propose a lightweight technique that trains only new token embeddings on axioms . they train only new embeddables and evaluate them on unseen tasks . |
| Outcome: | The proposed technique trains only new token embeddings on axioms and evaluates them on unseen tasks. |
Explanation-aware Soft Ensemble Empowers Large Language Model In-context Learning (2024.acl-long)
Copied to clipboard
Yue Yu, Jiaming Shen, Tianqi Liu, Zhen Qin, Jing Nathan Yan, Jialu Liu, Chao Zhang, Michael Bendersky
| Challenge: | Recent advances in natural language processing (NLP) have witnessed the remarkable capabilities of Large Language Models (LLMs). |
| Approach: | They propose an Explanation-Aware Soft Ensemble framework to empower in-context learning with Large language models. |
| Outcome: | The proposed framework can be used to enhance in-context learning on seven natural language understanding tasks and four varying-size LLMs. |
No Need for Explanations: LLMs can implicitly learn from mistakes in-context (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing literature assumes that correct answers to large language models must be accompanied by comprehensive rationales to be helpful. |
| Approach: | They propose to show incorrect answers to Large Language Models (LLMs) as a popular strategy to improve their performance in reasoning-intensive tasks. |
| Outcome: | The proposed approach outperforms chain-of-thought prompting in math reasoning tasks. |
Large Language Models are In-context Teachers for Knowledge Reasoning (2024.findings-emnlp)
Copied to clipboard
| Challenge: | In-context teaching is a method of providing in-concept example rationales to a student to reason over unseen cases. |
| Approach: | They propose to use an LLM's self-elicited explanations as in-context demonstrations to prompt a student to reason over unseen cases. |
| Outcome: | The proposed model outperforms human-crafted demonstrations on medical question answering and human-created models outperfect human-made demonstrations. |
PromptExplainer: Explaining Language Models through Prompt-based Learning (2024.findings-eacl)
Copied to clipboard
| Challenge: | Existing explanation methods rely on linear approximations, accentuating irrelevant input tokens. |
| Approach: | They propose a method that aligns the explanation process with the masked language modeling task of pretrained language models and leverages prompt-based learning to generate class-dependent explanations. |
| Outcome: | Extensive experiments show that PromptExplainer outperforms state-of-the-art explanation methods. |
Large Language Models Can be Lazy Learners: Analyze Shortcuts in In-Context Learning (2023.findings-acl)
Copied to clipboard
| Challenge: | Large language models (LLMs) have shown great potential for in-context learning, but their robustness and performance on downstream tasks remains limited. |
| Approach: | They propose to examine the reliance of LLMs on shortcuts or spurious correlations within prompts for downstream tasks and find larger models are more likely to utilize shortcuts in prompts during inference. |
| Outcome: | The proposed model is “lazy learner” and more likely to use shortcuts in prompts during inference. |
Large Language Models Might Not Care What You Are Saying: Prompt Format Beats Descriptions (2025.findings-emnlp)
Copied to clipboard
| Challenge: | In-context learning has improved performance of large language models, but descriptive instructions are still under-explored. |
| Approach: | They propose an ensemble prompt framework to describe selection criteria of multiple in-context examples. preliminary experiments on machine translation confirm that this framework boosts ICL performance. |
| Outcome: | The proposed framework improves on commonsense, math, logical reasoning and hallucination tasks with three LLMs. |