96 research outputs found
Word order evolves at similar rates in main and subordinate clauses
In syntactic change, it remains an open issue whether word orders are more conservative or innovative in subordinate clauses compared with main clauses. Using 47 dependency-annotated corpora and Bayesian phylogenetic inference, we explore the evolution of S/V, V/O, and S/O orders across main and subordinate clauses in Indo-European. Our results reveal similar rates of change across clause types, with no evidence for any inherent conservatism of subordinate or main clauses. Our models also support evolutionary biases towards SV, VO, and SO orders, consistent with theories of dependency length minimization that favor verb-medial orders and with theories of a subject preference that favor SO orders. Finally, our results show that while the word order in the proto-language cannot be estimated with any reasonable degree of certainty, the early history of the family was dominated by a moderate preference for SVO orders, with substantial uncertainty between VO and OV orders in both main and subordinate clauses
Self-Evaluation of Large Language Model based on Glass-box Features
The proliferation of open-source Large Language Models (LLMs) underscores the
pressing need for evaluation methods. Existing works primarily rely on external
evaluators, focusing on training and prompting strategies. However, a crucial
aspect - model-aware glass-box features - is overlooked. In this study, we
explore the utility of glass-box features under the scenario of
self-evaluation, namely applying an LLM to evaluate its own output. We
investigate various glass-box feature groups and discovered that the softmax
distribution serves as a reliable indicator for quality evaluation.
Furthermore, we propose two strategies to enhance the evaluation by
incorporating features derived from references. Experimental results on public
benchmarks validate the feasibility of self-evaluation of LLMs using glass-box
features.Comment: work in progres
BEVTrack: A Simple and Strong Baseline for 3D Single Object Tracking in Bird's-Eye View
3D Single Object Tracking (SOT) is a fundamental task of computer vision,
proving essential for applications like autonomous driving. It remains
challenging to localize the target from surroundings due to appearance
variations, distractors, and the high sparsity of point clouds. The spatial
information indicating objects' spatial adjacency across consecutive frames is
crucial for effective object tracking. However, existing trackers typically
employ point-wise representation with irregular formats, leading to
insufficient use of this important spatial knowledge. As a result, these
trackers usually require elaborate designs and solving multiple subtasks. In
this paper, we propose BEVTrack, a simple yet effective baseline that performs
tracking in Bird's-Eye View (BEV). This representation greatly retains spatial
information owing to its ordered structure and inherently encodes the implicit
motion relations of the target as well as distractors. To achieve accurate
regression for targets with diverse attributes (\textit{e.g.}, sizes and motion
patterns), BEVTrack constructs the likelihood function with the learned
underlying distributions adapted to different targets, rather than making a
fixed Laplace or Gaussian assumption as in previous works. This provides
valuable priors for tracking and thus further boosts performance. While only
using a single regression loss with a plain convolutional architecture,
BEVTrack achieves state-of-the-art performance on three large-scale datasets,
KITTI, NuScenes, and Waymo Open Dataset while maintaining a high inference
speed of about 200 FPS. The code will be released at
https://github.com/xmm-prio/BEVTrack.Comment: The code will be released at https://github.com/xmm-prio/BEVTrac
Global Analysis of Almost Periodic Solution of a Discrete Multispecies Mutualism System
This paper discusses a discrete multispecies Lotka-Volterra mutualism system. We first obtain the permanence
of the system. Assuming that the coefficients in the system are almost periodic sequences, we obtain the sufficient conditions for the existence of a unique almost periodic solution which is globally attractive. In particular, for the discrete two-species Lotka-Volterra mutualism system, the sufficient conditions for the existence of a unique uniformly asymptotically stable almost periodic solution are obtained. An example together with numerical simulation indicates the feasibility of the main result
RocketQAv2: A Joint Training Method for Dense Passage Retrieval and Passage Re-ranking
In various natural language processing tasks, passage retrieval and passage
re-ranking are two key procedures in finding and ranking relevant information.
Since both the two procedures contribute to the final performance, it is
important to jointly optimize them in order to achieve mutual improvement. In
this paper, we propose a novel joint training approach for dense passage
retrieval and passage re-ranking. A major contribution is that we introduce the
dynamic listwise distillation, where we design a unified listwise training
approach for both the retriever and the re-ranker. During the dynamic
distillation, the retriever and the re-ranker can be adaptively improved
according to each other's relevance information. We also propose a hybrid data
augmentation strategy to construct diverse training instances for listwise
training approach. Extensive experiments show the effectiveness of our approach
on both MSMARCO and Natural Questions datasets. Our code is available at
https://github.com/PaddlePaddle/RocketQA.Comment: EMNLP 202
BASES: Large-scale Web Search User Simulation with Large Language Model based Agents
Due to the excellent capacities of large language models (LLMs), it becomes
feasible to develop LLM-based agents for reliable user simulation. Considering
the scarcity and limit (e.g., privacy issues) of real user data, in this paper,
we conduct large-scale user simulation for web search, to improve the analysis
and modeling of user search behavior. Specially, we propose BASES, a novel user
simulation framework with LLM-based agents, designed to facilitate
comprehensive simulations of web search user behaviors. Our simulation
framework can generate unique user profiles at scale, which subsequently leads
to diverse search behaviors. To demonstrate the effectiveness of BASES, we
conduct evaluation experiments based on two human benchmarks in both Chinese
and English, demonstrating that BASES can effectively simulate large-scale
human-like search behaviors. To further accommodate the research on web search,
we develop WARRIORS, a new large-scale dataset encompassing web search user
behaviors, including both Chinese and English versions, which can greatly
bolster research in the field of information retrieval. Our code and data will
be publicly released soon
Investigating the Factual Knowledge Boundary of Large Language Models with Retrieval Augmentation
Knowledge-intensive tasks (e.g., open-domain question answering (QA)) require
a substantial amount of factual knowledge and often rely on external
information for assistance. Recently, large language models (LLMs) (e.g.,
ChatGPT), have demonstrated impressive prowess in solving a wide range of tasks
with world knowledge, including knowledge-intensive tasks. However, it remains
unclear how well LLMs are able to perceive their factual knowledge boundaries,
particularly how they behave when incorporating retrieval augmentation. In this
study, we present an initial analysis of the factual knowledge boundaries of
LLMs and how retrieval augmentation affects LLMs on open-domain QA. Specially,
we focus on three primary research questions and analyze them by examining QA
performance, priori judgement and posteriori judgement of LLMs. We show
evidence that LLMs possess unwavering confidence in their capabilities to
respond to questions and the accuracy of their responses. Furthermore,
retrieval augmentation proves to be an effective approach in enhancing LLMs'
awareness of knowledge boundaries, thereby improving their judgemental
abilities. Additionally, we also find that LLMs have a propensity to rely on
the provided retrieval results when formulating answers, while the quality of
these results significantly impacts their reliance. The code to reproduce this
work is available at https://github.com/RUCAIBox/LLM-Knowledge-Boundary
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