Björn Engelmann

Evaluating the interactions between users and systems presents many challenges. Simulation offers a reliable, re-usable, and repeatable methodology to explore how different users, user behaviours and/or retrieval systems impact performance. With Large Language Models and Generative AI now widely available and accessible, new affordances are possible. These allow researchers to create more ''realistic'' simulated users that can generate queries and judge items like humans, and to develop new retrieval systems where responses and interactions are conversational and based on retrieval augmented generation. This resource paper presents a community-led initiative to update the Simulation of Interactive Information Retrieval (SimIIR) Framework to enable the simulation of conversational search using LLMs. The largest update provides a conversational search workflow which involves a number of new possible interactions with a search system or agent -- enabling a host of new development and evaluation opportunities. Other developments include the Markovian Users, Cognitive States, LLM-based components for assessing snippets/documents/responses, generating queries, deciding on when to stop/continue, and PyTerrier integration. This paper aims to mark the release of SimIIR 3.0 and invites the community to build, extend, and use the resource.

Search query suggestions affect users’ interactions with search engines, which then influences the information they encounter. Thus, bias in search query suggestions can lead to exposure to biased search results and can impact opinion formation. This is especially critical in the political domain. Detecting and quantifying bias in web search engines is difficult due to its topic dependency, complexity, and subjectivity. The lack of context and phrasality of query suggestions emphasizes this problem. In a multi-step approach, we combine the benefits of large language models, pairwise comparison, and Elo-based scoring to identify and quantify bias in English search query suggestions. We apply our approach to the U.S. political news domain and compare bias in Google and Bing.

Simulating user interactions enables a more user-oriented evaluation of information retrieval (IR) systems. While user simulations are cost-efficient and reproducible, many approaches often lack fidelity regarding real user behavior. Most notably, current user models neglect the user's context, which is the primary driver of perceived relevance and the interactions with the search results. To this end, this work introduces the simulation of context-driven query reformulations. The proposed query generation methods build upon recent Large Language Model (LLM) approaches and consider the user's context throughout the simulation of a search session. Compared to simple context-free query generation approaches, these methods show better effectiveness and allow the simulation of more efficient IR sessions. Similarly, our evaluations consider more interaction context than current session-based measures and reveal interesting complementary insights in addition to the established evaluation protocols. We conclude with directions for future work and provide an entirely open experimental setup.

Björn Engelmann, Christin Katharina Kreutz, Fabian Haak, Philipp Schaer. Findings of the Association for Computational Linguistics: EMNLP 2024. 2024.

Christin Kreutz, Fabian Haak, Björn Engelmann, Philipp Schaer. Findings of the Association for Computational Linguistics ACL 2024. 2024.

Reading levels are highly individual and can depend on a text's language, a person's cognitive abilities, or knowledge on a topic. Text simplification is the task of rephrasing a text to better cater to the abilities of a specific target reader group. Simplification of scientific abstracts helps non-experts to access the core information by bypassing formulations that require domain or expert knowledge. This is especially relevant for, e.g., cancer patients reading about novel treatment options. The SimpleText lab hosts the simplification of scientific abstracts for non-experts (Task 3) to advance this field. We contribute three runs employing out-of-the-box summarization models (two based on T5, one based on PEGASUS) and one run using ChatGPT with complex phrase identification.