Embedding-based In-Context Prompt Training for Enhancing LLMs as Text Encoders (2026.findings-acl)
Copied to clipboard
| Challenge: | Large language models (LLMs) have been widely explored for embedding generation. |
| Approach: | They propose an embedding-based in-context prompt training strategy that leverages in-constext learning to generate high-quality embeddables while reducing computational burden. |
| Outcome: | The proposed method surpasses models trained on publicly available retrieval data and achieves state-of-the-art embedding performance on the MTEB benchmark. |
Similar Papers
Contrastive Prompting Enhances Sentence Embeddings in LLMs through Inference-Time Steering (2025.acl-long)
Copied to clipboard
| Challenge: | Existing studies focus on prompt engineering to encode the full semantics of a sentence into the embedding of the last token. |
| Approach: | They propose a technique that introduces an extra auxiliary prompt to elicit better sentence embedding . they propose to use the hidden state of the token as the sentence embedded in LLMs . |
| Outcome: | The proposed technique can improve performance of existing prompt-based methods on STS tasks and downstream classification tasks. |
Meta-Task Prompting Elicits Embeddings from Large Language Models (2024.acl-long)
Copied to clipboard
| Challenge: | Existing methods for large language modeling are based on task-related instructions or prompts. |
| Approach: | They propose a method for generating high-quality sentence embeddings from Large Language Models (LLMs) using meta-task prompts. |
| Outcome: | The proposed method produces high-quality sentences without fine-tuning . it excels on STS benchmarks and in downstream tasks, surpassing models with similar prompts . |
Causal2Vec: Improving Decoder-only LLMs as Embedding Models through a Contextual Token (2026.acl-long)
Copied to clipboard
| Challenge: | Existing methods modify attention mechanism to be bidirectional, undermining LLMs’ ability to extract semantic information acquired during pre-training. |
| Approach: | They propose a general-purpose embedding model that pre-encodes input text into a single Contextual token and then prepends it to the LLM's input sequence. |
| Outcome: | The proposed model improves performance of decoder-only large language models without altering their architectures or introducing significant computational overhead. |
Scaling Sentence Embeddings with Large Language Models (2024.findings-emnlp)
Copied to clipboard
| Challenge: | Current methods based on contrastive learning have generated high-quality sentence embeddings. |
| Approach: | They propose a method to enhance LLM performance on sentence embeddings with a one-word limitation. |
| Outcome: | The proposed method outperforms contrastive learning methods on sentence embeddings without fine-tuning and with fine-untun. |
Improving Text Embeddings with Large Language Models (2024.acl-long)
Copied to clipboard
| Challenge: | Existing methods for obtaining text embeddings require complex training pipelines . authors leverage proprietary LLMs to generate diverse synthetic data for text embeds based on 93 languages . |
| Approach: | They propose a method for obtaining high-quality text embeddings using only synthetic data and less than 1k training steps. |
| Outcome: | The proposed method achieves strong performance on competitive text embedding benchmarks without using any labeled data. |
Enhancing Text-to-SQL Capabilities of Large Language Models: A Study on Prompt Design Strategies (2023.findings-emnlp)
Copied to clipboard
Linyong Nan, Yilun Zhao, Weijin Zou, Narutatsu Ri, Jaesung Tae, Ellen Zhang, Arman Cohan, Dragomir Radev
| Challenge: | In-context learning (ICL) is a new approach to natural language processing tasks that rely on large language models to make predictions based on context . recent studies have shown that neural symbolic design is the preferred choice for question answering systems because of its limited working memory and unreliable long-term memory. |
| Approach: | They propose to extend in-context learning to question answering tasks that utilize structured knowledge sources and to explore various prompt design strategies for employing LLMs. |
| Outcome: | The proposed approach outperforms the state-of-the-art system by 2.5 points and the best fine-tuned system by 5.1 points on the Spider dataset. |
Tutor-ICL: Guiding Large Language Models for Improved In-Context Learning Performance (2024.findings-emnlp)
Copied to clipboard
| Challenge: | In-context learning (ICL) is a dominant paradigm in natural language processing. |
| Approach: | They propose a prompting method for classification tasks using exemplar answers in a *comparative format' they also propose introducing a test instance before the exemplars to improve performance . |
| Outcome: | The proposed method achieves up to 13.76% increase in accuracy on classification tasks across decoder-only and encoder-decoder LLMs. |
Training LLMs to be Better Text Embedders through Bidirectional Reconstruction (2025.emnlp-main)
Copied to clipboard
Chang Su, Dengliang Shi, Siyuan Huang, Jintao Du, Changhua Meng, Yu Cheng, Weiqiang Wang, Zhouhan Lin
| Challenge: | Existing text embedding approaches often leverage the embeddment of the final token, typically a reserved special token such as ‘[EOS]‘. |
| Approach: | They propose to add a new training stage before contrastive learning to enrich the semantics of the final token embedding. |
| Outcome: | The proposed training stage improves performance on the Massive Text Embedding Benchmark (MTEB), achieving new state-of-the-art results across different LLM base models and scales. |
Incubating Text Classifiers Following User Instruction with Nothing but LLM (2024.emnlp-main)
Copied to clipboard
| Challenge: | In this paper, we aim to generate text classification data given arbitrary class definitions . Traditional supervised text classification fine-tunes models on expensive human annotation . |
| Approach: | They propose a framework that can generate text classification data given arbitrary class definitions . they use instruction-to-data mappings and in-context augmentation to refine the framework . |
| Outcome: | The proposed framework outperforms existing methods on benchmarks and training data generation by prompt engineering. |
Towards Unified Task Embeddings Across Multiple Models: Bridging the Gap for Prompt-Based Large Language Models and Beyond (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing task embedding methods rely on fine-tuned, task-specific language models, which hinders their adaptability to prompt-guided Large Language Models (LLMs). |
| Approach: | They propose a framework for unified task embedding that harmonizes task embeds from various models within a single vector space. |
| Outcome: | The proposed framework harmonizes task embeddings from various models within a single vector space. |