75,082 research outputs found
Using the quantum probability ranking principle to rank interdependent documents
A known limitation of the Probability Ranking Principle (PRP) is that it does not cater for dependence between documents. Recently, the Quantum Probability Ranking Principle (QPRP) has been proposed, which implicitly captures dependencies between documents through “quantum interference”. This paper explores whether this new ranking principle leads to improved performance for subtopic retrieval, where novelty and diversity is required. In a thorough empirical investigation, models based on the PRP, as well as other recently proposed ranking strategies for subtopic retrieval (i.e. Maximal Marginal Relevance (MMR) and Portfolio Theory(PT)), are compared against the QPRP. On the given task, it is shown that the QPRP outperforms these other ranking strategies. And unlike MMR and PT, one of the main advantages of the QPRP is that no parameter estimation/tuning is required; making the QPRP both simple and effective. This research demonstrates that the application of quantum theory to problems within information retrieval can lead to significant improvements
Sum of ranking differences (SRD) to ensemble multivariate calibration model merits for tuning parameter selection and comparing calibration methods
Most multivariate calibration methods require selection of tuning parameters, such as partial least squares (PLS) or the Tikhonov regularization variant ridge regression (RR). Tuning parameter values determine the direction and magnitude of respective model vectors thereby setting the resultant predication abilities of the model vectors. Simultaneously, tuning parameter values establish the corresponding bias/variance and the underlying selectivity/sensitivity tradeoffs. Selection of the final tuning parameter is often accomplished through some form of cross-validation and the resultant root mean square error of cross-validation (RMSECV) values are evaluated. However, selection of a "good" tuning parameter with this one model evaluation merit is almost impossible. Including additional model merits assists tuning parameter selection to provide better balanced models as well as allowing for a reasonable comparison between calibration methods. Using multiple merits requires decisions to be made on how to combine and weight the merits into an information criterion. An abundance of options are possible. Presented in this paper is the sum of ranking differences (SRD) to ensemble a collection of model evaluation merits varying across tuning parameters. It is shown that the SRD consensus ranking of model tuning parameters allows automatic selection of the final model, or a collection of models if so desired. Essentially, the user's preference for the degree of balance between bias and variance ultimately decides the merits used in SRD and hence, the tuning parameter values ranked lowest by SRD for automatic selection. The SRD process is also shown to allow simultaneous comparison of different calibration methods for a particular data set in conjunction with tuning parameter selection. Because SRD evaluates consistency across multiple merits, decisions on how to combine and weight merits are avoided. To demonstrate the utility of SRD, a near infrared spectral data set and a quantitative structure activity relationship (QSAR) data set are evaluated using PLS and RR
Exploring Fine-tuning ChatGPT for News Recommendation
News recommendation systems (RS) play a pivotal role in the current digital
age, shaping how individuals access and engage with information. The fusion of
natural language processing (NLP) and RS, spurred by the rise of large language
models such as the GPT and T5 series, blurs the boundaries between these
domains, making a tendency to treat RS as a language task. ChatGPT, renowned
for its user-friendly interface and increasing popularity, has become a
prominent choice for a wide range of NLP tasks. While previous studies have
explored ChatGPT on recommendation tasks, this study breaks new ground by
investigating its fine-tuning capability, particularly within the news domain.
In this study, we design two distinct prompts: one designed to treat news RS as
the ranking task and another tailored for the rating task. We evaluate
ChatGPT's performance in news recommendation by eliciting direct responses
through the formulation of these two tasks. More importantly, we unravel the
pivotal role of fine-tuning data quality in enhancing ChatGPT's personalized
recommendation capabilities, and illustrates its potential in addressing the
longstanding challenge of the "cold item" problem in RS. Our experiments,
conducted using the Microsoft News dataset (MIND), reveal significant
improvements achieved by ChatGPT after fine-tuning, especially in scenarios
where a user's topic interests remain consistent, treating news RS as a ranking
task. This study illuminates the transformative potential of fine-tuning
ChatGPT as a means to advance news RS, offering more effective news consumption
experiences
Making Large Language Models Better Reasoners with Alignment
Reasoning is a cognitive process of using evidence to reach a sound
conclusion. The reasoning capability is essential for large language models
(LLMs) to serve as the brain of the artificial general intelligence agent.
Recent studies reveal that fine-tuning LLMs on data with the chain of thought
(COT) reasoning process can significantly enhance their reasoning capabilities.
However, we find that the fine-tuned LLMs suffer from an \textit{Assessment
Misalignment} problem, i.e., they frequently assign higher scores to subpar
COTs, leading to potential limitations in their reasoning abilities. To address
this problem, we introduce an \textit{Alignment Fine-Tuning (AFT)} paradigm,
which involves three steps: 1) fine-tuning LLMs with COT training data; 2)
generating multiple COT responses for each question, and categorizing them into
positive and negative ones based on whether they achieve the correct answer; 3)
calibrating the scores of positive and negative responses given by LLMs with a
novel constraint alignment loss. Specifically, the constraint alignment loss
has two objectives: a) Alignment, which guarantees that positive scores surpass
negative scores to encourage answers with high-quality COTs; b) Constraint,
which keeps the negative scores confined to a reasonable range to prevent the
model degradation. Beyond just the binary positive and negative feedback, the
constraint alignment loss can be seamlessly adapted to the ranking situations
when ranking feedback is accessible. Furthermore, we also delve deeply into
recent ranking-based alignment methods, such as DPO, RRHF, and PRO, and
discover that the constraint, which has been overlooked by these approaches, is
also crucial for their performance. Extensive experiments on four reasoning
benchmarks with both binary and ranking feedback demonstrate the effectiveness
of AFT.Comment: Large Language Models; Reasoning; Alignmen
Decomposition Based Search - A theoretical and experimental evaluation
In this paper we present and evaluate a search strategy called Decomposition
Based Search (DBS) which is based on two steps: subproblem generation and
subproblem solution. The generation of subproblems is done through value
ranking and domain splitting. Subdomains are explored so as to generate,
according to the heuristic chosen, promising subproblems first.
We show that two well known search strategies, Limited Discrepancy Search
(LDS) and Iterative Broadening (IB), can be seen as special cases of DBS. First
we present a tuning of DBS that visits the same search nodes as IB, but avoids
restarts. Then we compare both theoretically and computationally DBS and LDS
using the same heuristic. We prove that DBS has a higher probability of being
successful than LDS on a comparable number of nodes, under realistic
assumptions. Experiments on a constraint satisfaction problem and an
optimization problem show that DBS is indeed very effective if compared to LDS.Comment: 16 pages, 8 figures. LIA Technical Report LIA00203, University of
Bologna, 200
Top-Rank Enhanced Listwise Optimization for Statistical Machine Translation
Pairwise ranking methods are the basis of many widely used discriminative
training approaches for structure prediction problems in natural language
processing(NLP). Decomposing the problem of ranking hypotheses into pairwise
comparisons enables simple and efficient solutions. However, neglecting the
global ordering of the hypothesis list may hinder learning. We propose a
listwise learning framework for structure prediction problems such as machine
translation. Our framework directly models the entire translation list's
ordering to learn parameters which may better fit the given listwise samples.
Furthermore, we propose top-rank enhanced loss functions, which are more
sensitive to ranking errors at higher positions. Experiments on a large-scale
Chinese-English translation task show that both our listwise learning framework
and top-rank enhanced listwise losses lead to significant improvements in
translation quality.Comment: Accepted to CONLL 201
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