We present a novel text-driven approach for light field (LF) synthesis. Existing methods typically generate LFs from given images, requiring users to find reference images, which makes it difficult to construct the desired scene directly and limits scene diversity. Moreover, existing methods are mainly designed for limited baselines from training datasets, making it difficult to implement various viewpoint changes and consequently limiting the flexibility of motion. In contrast, our method directly synthesizes LFs from user-provided text descriptions by leveraging the scene understanding capabilities of a multi-modal large language model (LLM) and the generative power of a diffusion model. Given a text prompt describing the desired LF, the multimodal LLM extracts relevant information for LF synthesis, which then guides a diffusion model to produce diverse scenes and motions. This approach enables LF synthesis even with a pre-trained model not initially designed for this purpose, requiring only minimal fine-tuning. The proposed framework enables visually diverse LF synthesis with only text input. Experimental results demonstrate that the synthesized LFs exhibit geometric consistency and achieve advanced synthesis quality compared to existing methods.

Reranker improves retrieval performance by capturing document interactions. At one extreme, graph-aware adaptive retrieval (GAR) represents an information-rich regime, requiring a pre-computed document similarity graph in reranking. However, as such graphs are often unavailable, or incur quadratic memory costs even when available, graph-free rerankers leverage large language model (LLM) calls to achieve competitive performance. We introduce L2G, a novel framework that implicitly induces document graphs from listwise reranker logs. By converting reranker signals into a graph structure, L2G enables scalable graph-based retrieval without the overhead of explicit graph computation. Results on the TREC-DL and BEIR subset show that L2G matches the effectiveness of oracle-based graph methods, while incurring zero additional LLM calls.

Mitigating positional bias of language models (LMs) for listwise inputs is a well-known and important problem (e.g., lost-in-the-middle). While zero-shot order-invariant LMs have been proposed to solve this issue, their success on practical listwise problems has been limited. In this work, as a first contribution, we identify and overcome two limitations to make zero-shot invariant LMs more practical: (1) training and inference distribution mismatch arising from modifying positional ID assignments to enforce invariance, and (2) failure to adapt to mixture of order-invariant and sensitive inputs in practical listwise problems. Then, to overcome these issues we propose (1) RoToR, a zero-shot invariant LM for genuinely order-invariant inputs with minimal modifications of positional IDs, and (2) Selective Routing, an adaptive framework that handles both order-invariant and order-sensitive inputs in listwise tasks. On the Lost in the middle (LitM), Knowledge Graph QA (KGQA), and MMLU benchmarks, we show that RoToR with Selective Routing can effectively handle practical listwise input tasks in a zero-shot manner (https://github.com/soyoung97/RoToR)

Listwise reranking with large language models (LLMs) enhances top-ranked results in retrieval-based applications. Due to the limit in context size and high inference cost of long context, reranking is typically performed over a fixed size of small subsets, with the final ranking aggregated from these partial results. This fixed computation disregards query difficulty and document distribution, leading to inefficiencies. We propose AcuRank, an adaptive reranking framework that dynamically adjusts both the amount and target of computation based on uncertainty estimates over document relevance. Using a Bayesian TrueSkill model, we iteratively refine relevance estimates until reaching sufficient confidence levels, and our explicit modeling of ranking uncertainty enables principled control over reranking behavior and avoids unnecessary updates to confident predictions. Results on the TREC-DL and BEIR benchmarks show that our method consistently achieves a superior accuracy–efficiency trade-off and scales better with compute than fixed-computation baselines. These results highlight the effectiveness and generalizability of our method across diverse retrieval tasks and LLM-based reranking models.

We propose LISTT5, a novel reranking approach based on Fusion-in-Decoder (FiD) that handles multiple candidate passages at both train and inference time.We also introduce an efficient inference framework for listwise ranking based on m-ary tournament sort with output caching.We evaluate and compare our model on the BEIR benchmark for zero-shot retrieval task, demonstrating that LISTT5 (1) outperforms the state-of-the-art RankT5 baseline with a notable +1.3 gain in the average NDCG@10 score, (2) has an efficiency comparable to pointwise ranking models and surpasses the efficiency of previous listwise ranking models, and (3) overcomes the lost-in-the-middle problem of previous listwise rerankers.Our code, model checkpoints, and the evaluation framework are fully open-sourced at https: //github.com/soyoung97/ListT5.

Benchmarking the performance of information retrieval (IR) is mostly conducted with a fixed set of documents (static corpora).However, in realistic scenarios, this is rarely the case and the documents to be retrieved are constantly updated and added.In this paper, we focus on Generative Retrievals (GR), which apply autoregressive language models to IR problems, and explore their adaptability and robustness in dynamic scenarios.We also conduct an extensive evaluation of computational and memory efficiency, crucial factors for real-world deployment of IR systems handling vast and ever-changing document collections.Our results on the StreamingQA benchmark demonstrate that GR is more adaptable to evolving knowledge (4 -11%), robust in learning knowledge with temporal information, and efficient in terms of inference FLOPs (ˆ2), indexing time (ˆ6), and storage footprint (ˆ4) compared to Dual Encoders (DE), which are commonly used in retrieval systems.Our paper highlights the potential of GR for future use in practical IR systems within dynamic environments.

Soyoung Yoon, Sungjoon Park, Gyuwan Kim, Junhee Cho, Kihyo Park, Gyu Tae Kim, Minjoon Seo, Alice Oh. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2023.