  A standard paradigm for sentiment analysis is to rely on a singular LLM and
makes the decision in a single round under the framework of in-context
learning. This framework suffers the key disadvantage that the single-turn
output generated by a single LLM might not deliver the perfect decision, just
as humans sometimes need multiple attempts to get things right. This is
especially true for the task of sentiment analysis where deep reasoning is
required to address the complex linguistic phenomenon (e.g., clause
composition, irony, etc) in the input.
  To address this issue, this paper introduces a multi-LLM negotiation
framework for sentiment analysis. The framework consists of a reasoning-infused
generator to provide decision along with rationale, a explanation-deriving
discriminator to evaluate the credibility of the generator. The generator and
the discriminator iterate until a consensus is reached. The proposed framework
naturally addressed the aforementioned challenge, as we are able to take the
complementary abilities of two LLMs, have them use rationale to persuade each
other for correction.
  Experiments on a wide range of sentiment analysis benchmarks (SST-2, Movie
Review, Twitter, yelp, amazon, IMDB) demonstrate the effectiveness of proposed
approach: it consistently yields better performances than the ICL baseline
across all benchmarks, and even superior performances to supervised baselines
on the Twitter and movie review datasets.
