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Answer is All You Need: Instruction-following Text Embedding via Answering the Question (2402.09642v1)

Published 15 Feb 2024 in cs.CL

Abstract: This work aims to build a text embedder that can capture characteristics of texts specified by user instructions. Despite its tremendous potential to deploy user-oriented embeddings, none of previous approaches provides a concrete solution for it. This paper offers a new viewpoint, which treats the instruction as a question about the input text and encodes the expected answers to obtain the representation accordingly. Intuitively, texts with the same (implicit) semantics would share similar answers following the instruction, thus leading to more similar embeddings. Specifically, we propose InBedder that instantiates this embed-via-answering idea by only fine-tuning LLMs on abstractive question answering tasks. InBedder demonstrates significantly improved instruction-following capabilities according to our proposed instruction awareness tests and instruction robustness tests, when applied to both LLMs (e.g., llama-2-7b) and smaller encoder-based LMs (e.g., roberta-large). Additionally, our qualitative analysis of clustering outcomes, achieved by applying different instructions to the same corpus, demonstrates a high degree of interpretability.

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Citations (5)
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Summary

  • The paper introduces the InBedder framework, a method that generates text embeddings by answering user-defined instructions to capture semantic nuances.
  • The authors demonstrate that encoding strategies like avg-gen and 1st-gen outperform traditional prompt-based methods on instruction-driven benchmarks.
  • This approach enables personalized search, text clustering, and interpretable AI, paving the way for scalable and efficient NLP systems.

Answer is All You Need: Instruction-following Text Embedding via Answering the Question

The paper "Answer is All You Need: Instruction-following Text Embedding via Answering the Question" presents a novel approach to text embedding aimed explicitly at addressing the limitations of existing models in capturing user-specific characteristics. Traditional text embedders are primarily designed to encode general textual similarities without the capacity to follow user-defined instructions. This research introduces InBedder, a framework that creates text embeddings by generating responses to user-defined instructions.

The InBedder Framework

The authors propose a paradigm shift in text embedding by treating the instruction as a query and using the generated answers to derive the embeddings. The underlying hypothesis is that texts with similar semantics will yield similar responses to the same instructions, thereby leading to embeddings that are closer in the vector space.

To validate this hypothesis, the authors fine-tune existing LLMs on a union of 11 abstractive question-answering datasets, amounting to approximately 200,000 paragraph-question-answer triplets. The fine-tuning objective is to generate concise and informative responses, filtering out extraneous content to maintain answer brevity. This preprocessing culminates in an average answer length of 2.89 words.

Methodology and Evaluation

Encoding Methods: The paper explores several encoding strategies from the hidden states of LLMs:

  1. Direct Encoding:
    • Average of generation (avg-gen)
    • Average of prompt hidden states (avg-ppt)
    • Hidden states used to predict the first token in generations (1st-gen)
    • Last generation hidden states (last-gen)
    • Average of all hidden states (avg-all)
  2. Re-encoding:
    • Generating answers and re-encoding them using another lightweight sentence transformer.

Observations: The experiments reveal that generated answers (avg-gen) hold significantly more information pertinent to the instruction than the prompt-based hidden states (avg-ppt). Additionally, re-encoding approaches enhance embedding quality, with hidden states corresponding to the first generated token (1st-gen) demonstrating particularly strong performance in the fine-tuned models.

Performance Evaluation: The paper introduces new benchmarks for evaluating instruction-following capabilities, which include:

  • IntentEmotion: A triplet task evaluating embeddings based on intent and emotion.
  • InstructSTSB: An instruction-based semantic textual similarity task.
  • NYTClustering: Clustering tasks altering instruction parameters to test model adaptability.

The InBedder framework outperforms both traditional sentence transformers and other LLM-based embeddings across these benchmarks. Importantly, InBedder demonstrates a robust understanding of correct and implicit instructions while maintaining high-quality embeddings even under incorrect instructions.

Implications and Future Developments

Practical Implications: InBedder's ability to generate high-quality, instruction-specific text embeddings presents significant advancements for user-driven applications such as personalized search engines, customized text clustering, and interpretable AI systems. It offers a flexible tool for aligning text embeddings with user-defined criteria, thereby enhancing the contextual relevance and utility of NLP systems in complex, application-specific scenarios.

Theoretical Implications: The proposed instruction-following framework leverages the interpretability of LLMs by utilizing expected answer distributions rather than concatenated instruction-text pairs. This novel approach promotes a deeper semantic understanding and more effective embeddings in diverse NLP tasks.

Future Work: Future investigations could explore more efficient solutions for large-scale retrieval systems, potentially by integrating InBedder with query-dependent reranker systems to minimize latency. Additionally, enhancing the effectiveness of InBedder in generic embedding tasks through optimized prompt designs remains an open research area.

Conclusion

The "Answer is All You Need" paper introduces a substantial advancement in the domain of text embeddings by leveraging instruction-following capabilities. Through the InBedder framework, the paper demonstrates superior performance in instruction-awareness and robustness while maintaining competitive results in traditional tasks. This research sets a new trajectory for developing user-oriented embedding models, encouraging further exploration into more efficient, scalable, and effective solutions in NLP.

[Link to the paper's repository: https://github.com/zhang-yu-wei/InBedder]

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