TILDE vs. RankLLM

TILDE (Term Independent Likelihood moDEl) functions as a framework designed for the re-ranking and expansion of passages, leveraging BERT to enhance retrieval performance by combining sparse term matching with sophisticated contextual representations. The original TILDE version computes term weights across the entire BERT vocabulary, which often leads to extremely large index sizes. To address this limitation, TILDEv2 introduces a more efficient approach by calculating term weights exclusively for words present in the expanded passages, resulting in indexes that can be 99% smaller than those produced by the initial TILDE model. This improved efficiency is achieved by deploying TILDE as a passage expansion model, which enriches passages with top-k terms (for instance, the top 200) to improve their content quality. Furthermore, it provides scripts that streamline the processes of indexing collections, re-ranking BM25 results, and training models using datasets such as MS MARCO, thus offering a well-rounded toolkit for enhancing information retrieval tasks. In essence, TILDEv2 signifies a major leap forward in the management and optimization of passage retrieval systems, contributing to more effective and efficient information access strategies. This progression not only benefits researchers but also has implications for practical applications in various domains.

RankLLM is an advanced Python framework aimed at improving reproducibility within the realm of information retrieval research, with a specific emphasis on listwise reranking methods. The toolkit boasts a wide selection of rerankers, such as pointwise models exemplified by MonoT5, pairwise models like DuoT5, and efficient listwise models that are compatible with systems including vLLM, SGLang, or TensorRT-LLM. Additionally, it includes specialized iterations like RankGPT and RankGemini, which are proprietary listwise rerankers engineered for superior performance. The toolkit is equipped with vital components for retrieval processes, reranking activities, evaluation measures, and response analysis, facilitating smooth end-to-end workflows for users. Moreover, RankLLM's synergy with Pyserini enhances retrieval efficiency and guarantees integrated evaluation for intricate multi-stage pipelines, making the research process more cohesive. It also features a dedicated module designed for thorough analysis of input prompts and LLM outputs, addressing reliability challenges that can arise with LLM APIs and the variable behavior of Mixture-of-Experts (MoE) models. The versatility of RankLLM is further highlighted by its support for various backends, including SGLang and TensorRT-LLM, ensuring it works seamlessly with a broad spectrum of LLMs, which makes it an adaptable option for researchers in this domain. This adaptability empowers researchers to explore diverse model setups and strategies, ultimately pushing the boundaries of what information retrieval systems can achieve while encouraging innovative solutions to emerging challenges.