Retrieval-augmented generation (RAG) combines knowledge from domain-specific sources into large language models to ground answer generation. Current RAG systems lack customizable visibility on the context documents and the model's attentiveness towards such documents. We propose RAGViz, a RAG diagnosis tool that visualizes the attentiveness of the generated tokens in retrieved documents. With a built-in user interface, retrieval index, and Large Language Model (LLM) backbone, RAGViz provides two main functionalities: (1) token and document-level attention visualization and (2) generation comparison when adding and removing documents. We host a system demonstration of RAGViz configured with AnchorDR as the embedding model, Pile-CC training split as the data store, and LLaMa2-7B as the LLM backbone, with each of the above settings being customizable. The open source codebase is available at [https://github.com/TevinWang/ragviz](https://github.com/TevinWang/ragviz). The demo video is shown at [https://youtu.be/cTAbuTu6ur4](https://youtu.be/cTAbuTu6ur4). A live demonstration is available at [https://boston.lti.cs.cmu.edu/tevinw/ragviz/ui/](https://boston.lti.cs.cmu.edu/tevinw/ragviz/ui/). To access it, please use the API key *r0GkTCjVRDkc083IKQzklOIuXB51EMV8*. RAGViz is performant, as the total query time has a median of around 5 seconds for small queries with an average token length of 11.

RAGViz: Diagnose and Visualize Retrieval-Augmented Generation

This study investigates the existence of positional biases in Transformer-based language models for text representation learning, particularly in the context of web document retrieval. We build on previous research that demonstrated loss of information in the middle of input sequences for causal language models, extending it to the domain of embedding learning. We examine positional biases at multiple stages of the training pipeline for an encoder-decoder neural retrieval model, namely language model pre-training, contrastive pre-training, and contrastive fine-tuning. Experiments with the MS-MARCO document collection reveal that after contrastive pre-training the model already generates embeddings that better capture the beginning of the input content, with fine-tuning further aggravating this effect.

Dwell in the Beginning: How Language Models Embed Long Documents for Dense Retrieval

In multitask retrieval, a single retriever is trained to retrieve relevant contexts for multiple tasks. Despite its practical appeal, naive multitask retrieval lags behind task-specific retrieval in which a separate retriever is trained for each task. We show that it is possible to train a multitask retriever that outperforms task-specific retrievers by promoting task specialization. The main ingredients are: (1) a better choice of pretrained modelone that is explicitly optimized for multitaskingalong with compatible prompting, and (2) a novel adaptive learning method that encourages each parameter to specialize in a particular task. The resulting multitask retriever is highly performant on the KILT benchmark. Upon analysis, we find that the model indeed learns parameters that are more task-specialized compared to naive multitasking without prompting or adaptive learning.

Improving Multitask Retrieval by Promoting Task Specialization

In-Person Demo Session C (Riverfront Hall)

demo

## Welcome to EMNLP 2024! 
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In-Person Poster Session 2

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### Welcome!
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PS6 (In-person) Posters, Demo, Industry, Findings

**Welcome to EMNLP 2023!**

On behalf of the EMNLP 2023 Organizing Committee, I extend a warm and heartfelt welcome to all of you to the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP). It is with immense pleasure and excitement that we gather here in Singapore, a vibrant hub of innovation and technological advancement.

The conference program is packed with insightful presentations, thought-provoking workshops, and engaging networking opportunities. In addition to the technical sessions, we have also planned several social events that will provide you with the opportunity to connect with your colleagues.

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Yuji Matsumoto (RIKEN AIP) 
EMNLP 2023 General Chair

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Chenyan Xiong

3

Presentations

RAGViz: Diagnose and Visualize Retrieval-Augmented Generation

Dwell in the Beginning: How Language Models Embed Long Documents for Dense Retrieval

Improving Multitask Retrieval by Promoting Task Specialization

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